diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index e29b5b8..bc1eca6 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -17,8 +17,6 @@ jobs:
       - uses: actions/checkout@v4
 
       - uses: pnpm/action-setup@v4
-        with:
-          version: 10
 
       - uses: actions/setup-node@v4
         with:
@@ -32,4 +30,4 @@ jobs:
         run: pnpm -r run build
 
       - name: Run all tests
-        run: pnpm -r run test
+        run: pnpm exec vitest run
diff --git a/README.md b/README.md
index 8ce3c74..011d349 100644
--- a/README.md
+++ b/README.md
@@ -229,40 +229,17 @@ const model = await storage.load('my-agent-v1');
 
 ## Demos
 
-Five interactive demos showing the framework in action. Each one is a full package you can run locally.
+The [shared demo catalogue](packages/web/data/demos.json) supplies the homepage, documentation and static build with the same routes and metadata. See the documentation catalogue at `/docs/demos` when running the web app locally.
 
-### 2D Demos — Canvas + Charts
-
-| Demo | What it shows | Algorithm |
-|---|---|---|
-| **GridWorld** | Agent finds the shortest path in a 7×7 grid | Q-Table, DQN, PPO |
-| **CartPole** | Classic pole-balancing benchmark with Euler physics | DQN, PPO |
-| **MountainCar** | Agent discovers momentum strategy to climb a hill | DQN, PPO |
-
-### 3D Demos — React Three Fiber
-
-| Demo | What it shows | Tech |
-|---|---|---|
-| **CartPole 3D** | Metallic cart and pole, sunset environment, contact shadows | R3F + drei |
-| **Car Circuit** | 3D car learns to drive an oval circuit — chase cam, HUD, minimap, fading trail, 1x–50x speed slider | R3F + drei |
-
-### Run them locally
+**Circuit Racing** features two licensed 3D vehicles, a shared arcade simulation, learned imitation drivers, local checkpoints and races. Its evaluation reports include failures. The keyboard player mode shares the same physics as its opponents.
 
 ```bash
-git clone https://github.com/IgnitionAI/ignition.git
-cd ignition
 pnpm install
-pnpm -r run build
-
-# Pick your demo:
-pnpm --filter demo-gridworld dev       # http://localhost:3001
-pnpm --filter demo-cartpole dev        # http://localhost:3002
-pnpm --filter demo-mountaincar dev     # http://localhost:3003
-pnpm --filter demo-cartpole-3d dev     # http://localhost:3010
-pnpm --filter demo-car-circuit dev     # http://localhost:3020
+pnpm --filter demo-car-circuit dev
+# Other package names and available methods: packages/web/data/demos.json
 ```
 
-Each demo has: live 3D/2D visualization, Train/Inference/Stop/Reset controls, algorithm picker (DQN/PPO), live reward chart, and a code panel showing the exact API you'd write in your own project.
+The build currently excludes Target Chasing pending a separate compatibility check. The old oval Circuit tutorial remains a teaching example and uses an incompatible observation/action contract.
 
 ---
 
@@ -354,3 +331,57 @@ The codebase follows:
 Built by [@salim4n](https://github.com/salim4n) / [@IgnitionAI](https://github.com/IgnitionAI)
 
 **Star the repo** ⭐ if you think creative JS devs deserve proper RL tooling.
+
+## PPO rollout and episode boundaries
+
+The public training loop collects **128 transitions** before an automatic PPO
+update. Configure this with `env.train('ppo', { rolloutSize: 256 })`.
+`batchSize` controls optimizer minibatches, independently of `rolloutSize`.
+Calling `agent.train()` explicitly still updates all currently collected data,
+including a partial rollout. A singleton or constant-advantage batch keeps its
+raw advantages rather than removing its learning signal through normalization.
+
+`TrainingEnv.done()` remains required for compatibility. Existing environments
+continue to treat `done()` as a terminal condition. To distinguish external time
+limits, optionally provide `truncated(): boolean`. In that case termination
+falls back to `done() && !truncated()`. An optional `terminated(): boolean`
+overrides that fallback; when both explicit flags are true, termination takes
+precedence for value bootstrapping. Either flag resets the episode, retaining
+its final observation in the returned transition.
+
+PPO bootstraps nonterminal rollout ends and truncations from their actual next
+observation; its advantage trace stops at either episode boundary. DQN and
+Q-table also bootstrap truncations. Inference never trains. Manual environment
+reset and inference steps discard incomplete PPO rollouts; stop/resume alone
+retains them. Loading a PPO checkpoint discards pending transitions.
+
+Custom agents can optionally implement `shouldTrain()` to control automatic
+update cadence and `discardRollout()` to discard on-policy data when the training
+trajectory is interrupted. Agents without these hooks retain per-step updates.
+
+## Circuit evaluation (local demo API)
+
+The Circuit demo exposes `evaluateCircuit(policy, { policyId, circuit })` from
+its evaluation module. Pass a versioned policy identifier and `training` or
+`test`. The evaluator requests greedy actions through `IgnitionEnv.inferStep()`
+and never invokes the supplied policy's `train()` or `remember()`. Evaluate a
+checkpoint that is not being trained concurrently.
+
+Protocol `circuit-evaluation-v1` fixes two distinct oval geometries, three starting
+waypoints, a 1,500-transition limit per episode and a three-lap success criterion.
+It returns a JSON-serializable versioned report with per-episode outcomes,
+transition counts, completed laps and simulated lap times (50 ms per step).
+These are simulated times, not browser execution times. Preserve the report with
+its checkpoint and avoid training or selecting models on the reserved test track.
+
+The existing three-argument `CircuitEnv` constructor remains supported. Its
+optional fourth argument configures `maxSteps`, `targetLaps` and `startWaypoint`.
+Terminal failure/success and external time limits are distinct; `lastEpisode`
+retains the final metrics after an automatic reset. Completed laps require net
+forward progress from the selected starting position, rather than merely
+crossing the start line.
+
+This is the historical oval protocol. The current Circuit Racing experience uses
+its own `circuit-racing-v1`, `racing-observation-v1` and `circuit-race-v1` contracts.
+See [Circuit Racing](packages/demo-car-circuit/README.md) for current behavior,
+learned checkpoint reports and validation boundaries.
diff --git a/packages/backend-tfjs/src/agents/dqn.ts b/packages/backend-tfjs/src/agents/dqn.ts
index 4e11d1f..b4ceca4 100644
--- a/packages/backend-tfjs/src/agents/dqn.ts
+++ b/packages/backend-tfjs/src/agents/dqn.ts
@@ -22,6 +22,7 @@ export class DQNAgent implements AgentInterface {
   private trainStepCounter = 0;
   private actionSize: number;
   private bestReward = -Infinity;
+  private random: () => number = Math.random;
 
   constructor(private config: DQNConfig) {
     const result = DQNConfigSchema.safeParse(config);
@@ -48,6 +49,13 @@ export class DQNAgent implements AgentInterface {
       console.warn('[DQNAgent] Backend init warning:', err)
     );
 
+    if (config.seed !== undefined) {
+      let state = config.seed >>> 0;
+      this.random = () => {
+        state = (Math.imul(state, 1664525) + 1013904223) >>> 0;
+        return state / 4294967296;
+      };
+    }
     this.actionSize = actionSize;
     this.gamma = gamma;
     this.epsilon = epsilon;
@@ -56,16 +64,16 @@ export class DQNAgent implements AgentInterface {
     this.batchSize = batchSize;
     this.targetUpdateFrequency = targetUpdateFrequency;
 
-    this.model = buildQNetwork(inputSize, actionSize, hiddenLayers, lr);
-    this.targetModel = buildQNetwork(inputSize, actionSize, hiddenLayers, lr);
+    this.model = buildQNetwork(inputSize, actionSize, hiddenLayers, lr, config.seed);
+    this.targetModel = buildQNetwork(inputSize, actionSize, hiddenLayers, lr, config.seed);
     this.updateTargetModel();
 
-    this.memory = new ReplayBuffer(memorySize);
+    this.memory = new ReplayBuffer(memorySize, this.random);
   }
 
   async getAction(state: number[], greedy?: boolean): Promise<number> {
-    if (!greedy && Math.random() < this.epsilon) {
-      return Math.floor(Math.random() * this.actionSize);
+    if (!greedy && this.random() < this.epsilon) {
+      return Math.floor(this.random() * this.actionSize);
     }
 
     const stateTensor = tf.tensor2d([state]);
@@ -93,27 +101,31 @@ export class DQNAgent implements AgentInterface {
     const states = batch.map(e => e.state);
     const nextStates = batch.map(e => e.nextState);
 
-    const stateTensor = tf.tensor2d(states);
-    const nextStateTensor = tf.tensor2d(nextStates);
-
-    const qValues = this.model.predict(stateTensor) as tf.Tensor2D;
-    const nextQValues = this.targetModel.predict(nextStateTensor) as tf.Tensor2D;
-
-    const qArray = qValues.arraySync() as number[][];
-    const nextQArray = nextQValues.arraySync() as number[][];
-
-    const updatedQ = qArray.map((q, i) => {
-      const { action, reward, terminated, truncated } = batch[i];
-      const done = terminated || truncated;
-      const a = action as number;
-      q[a] = done ? reward : reward + this.gamma * Math.max(...nextQArray[i]);
-      return q;
+    const { stateTensor, targetTensor } = tf.tidy(() => {
+      const stateTensor = tf.tensor2d(states);
+      const nextStateTensor = tf.tensor2d(nextStates);
+      const qValues = this.model.predict(stateTensor) as tf.Tensor2D;
+      const nextQValues = this.targetModel.predict(nextStateTensor) as tf.Tensor2D;
+      const qArray = qValues.arraySync() as number[][];
+      const nextQArray = nextQValues.arraySync() as number[][];
+      const onlineNextArray = this.config.doubleQ
+        ? (this.model.predict(nextStateTensor) as tf.Tensor2D).arraySync() as number[][]
+        : undefined;
+      const updatedQ = qArray.map((q, i) => {
+        const { action, reward, terminated } = batch[i];
+        const online = onlineNextArray?.[i];
+        const selectedAction = online ? online.indexOf(Math.max(...online)) : -1;
+        const bootstrap = online ? nextQArray[i][selectedAction] : Math.max(...nextQArray[i]);
+        q[action as number] = terminated ? reward : reward + this.gamma * bootstrap;
+        return q;
+      });
+      return { stateTensor, targetTensor: tf.tensor2d(updatedQ) };
     });
-
-    const targetTensor = tf.tensor2d(updatedQ);
-    await this.model.fit(stateTensor, targetTensor, { epochs: 1, verbose: 0 });
-
-    tf.dispose([stateTensor, nextStateTensor, qValues, nextQValues, targetTensor]);
+    try {
+      await this.model.fit(stateTensor, targetTensor, { epochs: 1, verbose: 0, ...(this.config.seed === undefined ? {} : { shuffle: false }) });
+    } finally {
+      tf.dispose([stateTensor, targetTensor]);
+    }
 
     if (this.epsilon > this.minEpsilon) {
       this.epsilon *= this.epsilonDecay;
@@ -127,7 +139,7 @@ export class DQNAgent implements AgentInterface {
 
   reset(): void {
     this.epsilon = this.config.epsilon ?? 1.0;
-    this.memory = new ReplayBuffer(this.config.memorySize);
+    this.memory = new ReplayBuffer(this.config.memorySize, this.random);
     this.trainStepCounter = 0;
   }
 
@@ -139,8 +151,7 @@ export class DQNAgent implements AgentInterface {
   async loadFromHub(repoId: string, modelPath = 'model.json'): Promise<void> {
     console.log(`[DQN] Loading model from HF Hub: ${repoId}`);
     const loadedModel = await loadModelFromHub(repoId, modelPath);
-    this.model = loadedModel as tf.Sequential;
-    await this.updateTargetModel();
+    this.replaceModel(loadedModel as tf.Sequential);
   }
 
   async saveCheckpoint(repoId: string, token: string, checkpointName: string): Promise<void> {
@@ -164,8 +175,7 @@ export class DQNAgent implements AgentInterface {
     const modelPath = `model_${checkpointName}/model.json`;
     console.log(`[DQN] Loading checkpoint "${checkpointName}" from HF Hub...`);
     const model = await loadModelFromHub(repoId, modelPath);
-    this.model = model as tf.Sequential;
-    await this.updateTargetModel();
+    this.replaceModel(model as tf.Sequential);
     console.log(`[DQN] ✅ Checkpoint "${checkpointName}" loaded`);
   }
 
@@ -185,7 +195,7 @@ export class DQNAgent implements AgentInterface {
     if (!provider) {
       throw new Error('[DQN] No storageProvider configured. Pass one in DQNConfig.');
     }
-    return provider.save(modelId, this.model, metadata);
+    return provider.save(modelId, this.model, { ...metadata, algorithm: this.config.doubleQ ? 'double-dqn' : 'dqn' });
   }
 
   /**
@@ -198,8 +208,16 @@ export class DQNAgent implements AgentInterface {
       throw new Error('[DQN] No storageProvider configured. Pass one in DQNConfig.');
     }
     const loaded = await provider.load(modelId);
-    this.model = loaded as tf.Sequential;
-    await this.updateTargetModel();
+    this.replaceModel(loaded as tf.Sequential);
+  }
+
+  private replaceModel(loaded: tf.Sequential): void {
+    if (loaded === this.model) { this.targetModel.setWeights(loaded.getWeights()); return; }
+    try { this.targetModel.setWeights(loaded.getWeights()); }
+    catch (error) { loaded.optimizer?.dispose(); loaded.dispose(); throw error; }
+    this.model.optimizer?.dispose();
+    this.model.dispose();
+    this.model = loaded;
   }
 
   getState(): Record<string, unknown> {
@@ -227,7 +245,9 @@ export class DQNAgent implements AgentInterface {
 
   dispose(): void {
     console.log(`[DQN] Disposing model...`);
+    this.model?.optimizer?.dispose();
     this.model?.dispose();
+    this.targetModel?.optimizer?.dispose();
     this.targetModel?.dispose();
     this.memory = new ReplayBuffer(0);
     console.log(`[DQN] ✅ DQNAgent disposed`);
diff --git a/packages/backend-tfjs/src/agents/ppo.ts b/packages/backend-tfjs/src/agents/ppo.ts
index edda2a8..2465103 100644
--- a/packages/backend-tfjs/src/agents/ppo.ts
+++ b/packages/backend-tfjs/src/agents/ppo.ts
@@ -214,12 +214,16 @@ export class PPOAgent implements AgentInterface {
     });
   }
 
-  /**
-   * Mettre à jour l'acteur et le critic sur les données collectées.
-   * Vide le buffer de rollout à la fin (algorithme on-policy).
-   *
-   * Appelé typiquement à la fin de chaque épisode ou après N steps.
-   */
+  /** Discard data when the training trajectory is interrupted. */
+  discardRollout(): void {
+    this.rollout = [];
+  }
+
+  shouldTrain(): boolean {
+    return this.rollout.length >= (this.config.rolloutSize ?? 128);
+  }
+
+  /** Explicitly update all collected transitions, including partial rollouts. */
   async train(): Promise<void> {
     const n = this.rollout.length;
     if (n === 0) return;
@@ -254,6 +258,7 @@ export class PPOAgent implements AgentInterface {
 
     // Vider le rollout — PPO est on-policy
     this.rollout = [];
+    this.trainStepCounter++;
   }
 
   // -------------------------------------------------------------------------
@@ -279,21 +284,26 @@ export class PPOAgent implements AgentInterface {
     const advantages = new Array<number>(n);
     const returns = new Array<number>(n);
 
-    let nextValue = 0; // V(s_{T+1}) = 0 pour l'état terminal
+    // Evaluate the actual successor before any update, including truncated
+    // episodes and the end of a nonterminal rollout.
+    const nextValues = tf.tidy(() => {
+      const states = tf.tensor2d(this.rollout.map(e => e.nextState));
+      return Array.from((this.criticNet.predict(states) as tf.Tensor).dataSync());
+    });
     let lastGAE = 0;
 
     for (let t = n - 1; t >= 0; t--) {
       const { reward, terminated, truncated, value } = this.rollout[t];
-      const mask = (terminated || truncated) ? 0 : 1;
+      const bootstrapMask = terminated ? 0 : 1;
+      const traceMask = (terminated || truncated) ? 0 : 1;
 
       // Erreur TD
-      const delta = reward + this.gamma * nextValue * mask - value;
+      const delta = reward + this.gamma * nextValues[t] * bootstrapMask - value;
       // Accumulation GAE
-      lastGAE = delta + this.gamma * this.gaeLambda * mask * lastGAE;
+      lastGAE = delta + this.gamma * this.gaeLambda * traceMask * lastGAE;
 
       advantages[t] = lastGAE;
       returns[t] = lastGAE + value;
-      nextValue = value;
     }
 
     // Normalisation des avantages
@@ -304,7 +314,9 @@ export class PPOAgent implements AgentInterface {
 
     return {
       returns,
-      advantages: advantages.map(a => (a - mean) / std),
+      advantages: n > 1 && std > 1e-8
+        ? advantages.map(a => (a - mean) / std)
+        : advantages,
     };
   }
 
@@ -456,6 +468,9 @@ export class PPOAgent implements AgentInterface {
     }
     const actor = await provider.load(`${modelId}/actor`);
     const critic = await provider.load(`${modelId}/critic`);
+    this.actorNet.dispose();
+    this.criticNet.dispose();
+    this.rollout = [];
     this.actorNet = actor as tf.Sequential;
     this.criticNet = critic as tf.Sequential;
     console.log(`[PPO] ✅ Loaded model ${modelId}`);
@@ -464,6 +479,8 @@ export class PPOAgent implements AgentInterface {
   dispose(): void {
     this.actorNet?.dispose();
     this.criticNet?.dispose();
+    this.actorOptimizer.dispose();
+    this.criticOptimizer.dispose();
     this.rollout = [];
   }
 }
diff --git a/packages/backend-tfjs/src/agents/qtable.ts b/packages/backend-tfjs/src/agents/qtable.ts
index 0efc7c0..1f7d984 100644
--- a/packages/backend-tfjs/src/agents/qtable.ts
+++ b/packages/backend-tfjs/src/agents/qtable.ts
@@ -140,8 +140,8 @@ export class QTableAgent implements AgentInterface {
   async train(): Promise<void> {
     if (!this.lastExperience) return;
 
-    const { state, action, reward, nextState, terminated, truncated } = this.lastExperience;
-    const done = terminated || truncated;
+    const { state, action, reward, nextState, terminated } = this.lastExperience;
+    const done = terminated;
     const a = action as number;
     const sIdx = this.stateToIndex(state);
     const sNextIdx = this.stateToIndex(nextState);
diff --git a/packages/backend-tfjs/src/defaults.ts b/packages/backend-tfjs/src/defaults.ts
index 57e09f9..d5fe5ff 100644
--- a/packages/backend-tfjs/src/defaults.ts
+++ b/packages/backend-tfjs/src/defaults.ts
@@ -35,6 +35,7 @@ export const QTABLE_DEFAULTS: Record<string, unknown> = {
 
 export const ALGORITHM_DEFAULTS: Record<string, Record<string, unknown>> = {
   dqn: DQN_DEFAULTS,
+  'double-dqn': { ...DQN_DEFAULTS, doubleQ: true },
   ppo: PPO_DEFAULTS,
   qtable: QTABLE_DEFAULTS,
 };
diff --git a/packages/backend-tfjs/src/ignition-env-tfjs.ts b/packages/backend-tfjs/src/ignition-env-tfjs.ts
index 2626397..9b5c045 100644
--- a/packages/backend-tfjs/src/ignition-env-tfjs.ts
+++ b/packages/backend-tfjs/src/ignition-env-tfjs.ts
@@ -6,6 +6,7 @@ import { ALGORITHM_DEFAULTS } from './defaults';
 import type { DQNConfig, PPOConfig, QTableConfig } from './types';
 
 const FACTORIES: Record<string, AgentFactory> = {
+  'double-dqn': (config) => new DQNAgent({ ...(config as unknown as DQNConfig), doubleQ: true }),
   dqn: (config) => new DQNAgent(config as unknown as DQNConfig),
   ppo: (config) => new PPOAgent(config as unknown as PPOConfig),
   qtable: (config) => new QTableAgent(config as unknown as QTableConfig),
diff --git a/packages/backend-tfjs/src/memory/ReplayBuffer.ts b/packages/backend-tfjs/src/memory/ReplayBuffer.ts
index bec1b13..e73742a 100644
--- a/packages/backend-tfjs/src/memory/ReplayBuffer.ts
+++ b/packages/backend-tfjs/src/memory/ReplayBuffer.ts
@@ -8,7 +8,7 @@ export class ReplayBuffer {
   private _size = 0;
   private readonly capacity: number;
 
-  constructor(capacity = 10000) {
+  constructor(capacity = 10000, private readonly random: () => number = Math.random) {
     this.capacity = capacity;
     this.buffer = new Array(capacity);
   }
@@ -24,7 +24,7 @@ export class ReplayBuffer {
     const n = Math.min(batchSize, this._size);
     const sampled: Experience[] = [];
     for (let i = 0; i < n; i++) {
-      sampled.push(this.buffer[Math.floor(Math.random() * this._size)]);
+      sampled.push(this.buffer[Math.floor(this.random() * this._size)]);
     }
     return sampled;
   }
diff --git a/packages/backend-tfjs/src/model/BuildMLP.ts b/packages/backend-tfjs/src/model/BuildMLP.ts
index 0077b0a..de2581f 100644
--- a/packages/backend-tfjs/src/model/BuildMLP.ts
+++ b/packages/backend-tfjs/src/model/BuildMLP.ts
@@ -14,13 +14,15 @@ export function buildQNetwork(
   inputSize: number,
   outputSize: number,
   hiddenLayers: number[] = [24, 24],
-  lr: number = 0.001
+  lr: number = 0.001,
+  seed?: number
 ): tf.Sequential {
   const model = tf.sequential();
 
   // Input layer
   model.add(tf.layers.dense({
     inputShape: [inputSize],
+    ...(seed === undefined ? {} : {kernelInitializer: tf.initializers.glorotUniform({seed})}),
     units: hiddenLayers[0],
     activation: 'relu',
   }));
@@ -29,6 +31,7 @@ export function buildQNetwork(
   for (let i = 1; i < hiddenLayers.length; i++) {
     model.add(tf.layers.dense({
       units: hiddenLayers[i],
+      ...(seed === undefined ? {} : {kernelInitializer: tf.initializers.glorotUniform({seed: seed + i})}),
       activation: 'relu',
     }));
   }
@@ -36,6 +39,7 @@ export function buildQNetwork(
   // Output layer (linear activation for Q-values)
   model.add(tf.layers.dense({
     units: outputSize,
+    ...(seed === undefined ? {} : {kernelInitializer: tf.initializers.glorotUniform({seed: seed + hiddenLayers.length})}),
     activation: 'linear',
   }));
 
diff --git a/packages/backend-tfjs/src/schemas.ts b/packages/backend-tfjs/src/schemas.ts
index 11a8aa2..857e210 100644
--- a/packages/backend-tfjs/src/schemas.ts
+++ b/packages/backend-tfjs/src/schemas.ts
@@ -6,6 +6,8 @@ const TFBackendSchema = z.enum(['webgpu', 'webgl', 'cpu', 'wasm', 'node', 'auto'
 
 export const DQNConfigSchema = z
   .object({
+    doubleQ: z.boolean().optional(),
+    seed: z.number().int().min(1).max(1000000).optional(),
     backend: TFBackendSchema,
     inputSize: z
       .number()
@@ -73,6 +75,7 @@ export type DQNConfig = z.infer<typeof DQNConfigSchema>;
 // ─── PPOConfig ────────────────────────────────────────────────────────────────
 
 export const PPOConfigSchema = z.object({
+  rolloutSize: z.number().int().positive().optional(),
   backend: TFBackendSchema,
   inputSize: z
     .number()
diff --git a/packages/backend-tfjs/src/types.ts b/packages/backend-tfjs/src/types.ts
index d10c952..98e2627 100644
--- a/packages/backend-tfjs/src/types.ts
+++ b/packages/backend-tfjs/src/types.ts
@@ -5,6 +5,10 @@ import type { ModelStorageProvider } from '@ignitionai/storage';
 export type { AgentInterface, Experience } from '@ignitionai/core';
 
 export interface DQNConfig {
+  /** Online action selection with target-network evaluation (default: false). */
+  doubleQ?: boolean;
+  /** Reproducible initialization, exploration and replay sampling when supplied. */
+  seed?: number;
   backend?: TFBackend;             // TF.js backend to use (default: 'auto')
   inputSize: number;              // Dimension du vecteur d'état
   actionSize: number;             // Nombre d'actions discrètes
@@ -21,6 +25,8 @@ export interface DQNConfig {
 }
 
 export interface PPOConfig {
+  /** Transitions collected before automatic training (default: 128). */
+  rolloutSize?: number;
   backend?: TFBackend;             // TF.js backend to use (default: 'auto')
   inputSize: number;        // Dimension du vecteur d'état
   actionSize: number;       // Nombre d'actions discrètes
diff --git a/packages/backend-tfjs/test/double-dqn-api.test.ts b/packages/backend-tfjs/test/double-dqn-api.test.ts
new file mode 100644
index 0000000..01dd3e7
--- /dev/null
+++ b/packages/backend-tfjs/test/double-dqn-api.test.ts
@@ -0,0 +1,34 @@
+import { expect, it, vi } from 'vitest';
+import * as tf from '@tensorflow/tfjs';
+import { IgnitionEnvTFJS } from '../src/ignition-env-tfjs';
+import type { ModelStorageProvider } from '@ignitionai/storage';
+
+it('creates, trains, saves and greedily infers Double DQN through IgnitionEnv', async () => {
+  await tf.setBackend('cpu');
+  let metadata: Record<string, unknown> | undefined;
+  let saved: tf.Sequential | undefined;
+  const provider: ModelStorageProvider = {
+    async save(_id, model, meta) { saved = model as tf.Sequential; metadata = meta; return 'memory'; },
+    async load() { throw new Error('Not used'); },
+    async list() { return []; }, async exists() { return true; }, async delete() {},
+  };
+  const game = { actions: 2, observe: () => [0], step: (_action: number) => {},
+    reward: () => 1, done: () => true, reset: () => {} };
+  const env = new IgnitionEnvTFJS(game);
+  try {
+    env.train('double-dqn', { backend: 'cpu', batchSize: 1, memorySize: 2, storageProvider: provider });
+    env.stop();
+    await env.step();
+    await env.save('double', { algorithm: 'wrong-caller-label' });
+    expect(metadata?.algorithm).toBe('double-dqn');
+    expect(env.agent?.getState?.().trainStepCounter).toBe(1);
+    const tensors = tf.memory().numTensors;
+    const failedFit = vi.spyOn(saved!, 'fit').mockRejectedValueOnce(new Error('injected fit failure'));
+    await expect(env.agent!.train()).rejects.toThrow('injected fit failure');
+    expect(tf.memory().numTensors).toBe(tensors);
+    failedFit.mockRestore();
+    const before = saved!.getWeights().map(t => Array.from(t.dataSync()));
+    await env.inferStep();
+    expect(saved!.getWeights().map(t => Array.from(t.dataSync()))).toEqual(before);
+  } finally { env.stop(); env.agent?.dispose?.(); }
+});
diff --git a/packages/backend-tfjs/test/double-dqn.test.ts b/packages/backend-tfjs/test/double-dqn.test.ts
new file mode 100644
index 0000000..d65a83c
--- /dev/null
+++ b/packages/backend-tfjs/test/double-dqn.test.ts
@@ -0,0 +1,39 @@
+import { expect, it } from 'vitest';
+import * as tf from '@tensorflow/tfjs';
+import { DQNAgent } from '../src/agents/dqn';
+import type { ModelStorageProvider } from '@ignitionai/storage';
+
+it.each([false, true])('separates online selection from target evaluation: doubleQ=%s', async doubleQ => {
+  await tf.setBackend('cpu');
+  const net = tf.sequential();
+  net.add(tf.layers.dense({ inputShape: [1], units: 8, activation: 'relu', kernelInitializer: 'zeros' }));
+  net.add(tf.layers.dense({ units: 2, kernelInitializer: 'zeros' }));
+  net.compile({ optimizer: tf.train.sgd(0.1), loss: 'meanSquaredError' });
+  const setBias = (bias: number[]) => tf.tidy(() => {
+    const weights = net.getWeights();
+    net.setWeights([...weights.slice(0, -1), tf.tensor1d(bias)]);
+  });
+  setBias([1, 4]);
+  let learned = 0;
+  let savedMetadata: Record<string, unknown> | undefined;
+  const provider: ModelStorageProvider = {
+    async load() { return net; },
+    async save(_id, model, metadata) {
+      learned = tf.tidy(() => (model.predict(tf.tensor2d([[0]])) as tf.Tensor).dataSync()[0]);
+      savedMetadata = metadata;
+      return 'memory';
+    }, async list() { return []; }, async exists() { return true; }, async delete() {},
+  };
+  const agent = new DQNAgent({ inputSize: 1, actionSize: 2, hiddenLayers: [8], batchSize: 1,
+    memorySize: 2, gamma: 1, backend: 'cpu', storageProvider: provider, doubleQ });
+  try {
+    await agent.load('fixture'); // target values [1,4]
+    setBias([3, 2]); // online chooses action 0; target would choose action 1
+    expect(await agent.getAction([0], true)).toBe(0);
+    agent.remember({ state: [0], action: 0, nextState: [0], reward: 0, terminated: false, truncated: true });
+    await agent.train();
+    await agent.save('result');
+    expect(learned).toBeCloseTo(doubleQ ? 2.8 : 3.1, 4);
+    expect(savedMetadata?.algorithm).toBe(doubleQ ? 'double-dqn' : 'dqn');
+  } finally { agent.dispose(); }
+});
diff --git a/packages/backend-tfjs/test/dqn-load-memory.test.ts b/packages/backend-tfjs/test/dqn-load-memory.test.ts
new file mode 100644
index 0000000..25d71f2
--- /dev/null
+++ b/packages/backend-tfjs/test/dqn-load-memory.test.ts
@@ -0,0 +1,15 @@
+import { expect, it } from 'vitest';
+import * as tf from '@tensorflow/tfjs';
+import { DQNAgent } from '../src/agents/dqn';
+import { buildQNetwork } from '../src/model/BuildMLP';
+it('releases replaced networks and owned optimizers after repeated loads',async()=>{
+  await tf.setBackend('cpu'); const baseline=tf.memory().numTensors;
+  const agent=new DQNAgent({inputSize:2,actionSize:2,hiddenLayers:[8],backend:'cpu',storageProvider:{
+    async load(){return buildQNetwork(2,2,[8]);},async save(){return'memory';},async list(){return[];},async exists(){return true;},async delete(){},
+  }});
+  await agent.load('first');const loaded=tf.memory().numTensors;
+  for(let i=0;i<3;i++)await agent.load('next');
+  const repeated=tf.memory().numTensors;
+  agent.dispose();
+  expect(repeated).toBe(loaded);expect(tf.memory().numTensors).toBe(baseline);
+});
diff --git a/packages/backend-tfjs/test/dqn-seed.test.ts b/packages/backend-tfjs/test/dqn-seed.test.ts
new file mode 100644
index 0000000..b3feb7d
--- /dev/null
+++ b/packages/backend-tfjs/test/dqn-seed.test.ts
@@ -0,0 +1,23 @@
+import { expect, it } from 'vitest';
+import * as tf from '@tensorflow/tfjs';
+import { DQNAgent } from '../src/agents/dqn';
+it('repeats initialization, exploration and replay updates with a supplied seed', async () => {
+  await tf.setBackend('cpu');
+  const snapshots: unknown[] = [];
+  const storageProvider = {
+    async save(_id: string, model: unknown) { snapshots.push((model as tf.Sequential).getWeights().map(t=>Array.from(t.dataSync())));return 'memory'; },
+    async load():Promise<never>{throw new Error('unused');},async list(){return[];},async exists(){return true;},async delete(){},
+  };
+  const config = { inputSize: 2, actionSize: 2, hiddenLayers: [8], batchSize: 64, memorySize: 128, storageProvider, backend: 'cpu' as const, seed: 29 };
+  const a = new DQNAgent(config), b = new DQNAgent(config);
+  try {
+    for (let i=0;i<70;i++) {
+      expect(await a.getAction([i/10,1])).toBe(await b.getAction([i/10,1]));
+      const exp={state:[i/10,1],nextState:[(i+1)/10,1],action:i%2,reward:i/10,terminated:false,truncated:false};
+      a.remember(exp); b.remember(exp); await a.train(); await b.train();
+    }
+    for (let i=0;i<10;i++) expect(await a.getAction([i/10,1],true)).toBe(await b.getAction([i/10,1],true));
+    expect(a.getState()).toEqual(b.getState());
+    await a.save("a");await b.save("b");expect(snapshots[0]).toEqual(snapshots[1]);
+  } finally {a.dispose();b.dispose();}
+});
diff --git a/packages/backend-tfjs/test/dqn-truncation.test.ts b/packages/backend-tfjs/test/dqn-truncation.test.ts
new file mode 100644
index 0000000..55709de
--- /dev/null
+++ b/packages/backend-tfjs/test/dqn-truncation.test.ts
@@ -0,0 +1,31 @@
+import { expect, it } from 'vitest';
+import * as tf from '@tensorflow/tfjs';
+import { DQNAgent } from '../src/agents/dqn';
+import type { ModelStorageProvider } from '@ignitionai/storage';
+
+it.each([false, true])('DQN bootstraps unless terminated=%s', async (terminated) => {
+  await tf.setBackend('cpu');
+  const net = tf.sequential();
+  net.add(tf.layers.dense({ inputShape: [1], units: 8, activation: 'relu', kernelInitializer: 'zeros' }));
+  net.add(tf.layers.dense({ units: 2, kernelInitializer: 'zeros',
+    biasInitializer: tf.initializers.constant({ value: 2 }) }));
+  net.compile({ optimizer: 'sgd', loss: 'meanSquaredError' });
+  let learned = 0;
+  const provider: ModelStorageProvider = {
+    async load() { return net; },
+    async save(_id, model) {
+      learned = tf.tidy(() => (model.predict(tf.tensor2d([[0]])) as tf.Tensor).dataSync()[0]);
+      return 'memory';
+    }, async list() { return []; }, async exists() { return true; }, async delete() {},
+  };
+  const agent = new DQNAgent({ inputSize: 1, actionSize: 2, hiddenLayers: [8], batchSize: 1,
+    memorySize: 2, gamma: 0.9, backend: 'cpu', storageProvider: provider });
+  try {
+    await agent.load('fixture');
+    agent.remember({ state: [0], action: 0, nextState: [0], reward: 0.5, terminated, truncated: !terminated });
+    await agent.train();
+    await agent.save('result');
+    if (terminated) expect(learned).toBeLessThan(2);
+    else expect(learned).toBeGreaterThan(2);
+  } finally { agent.dispose(); }
+});
diff --git a/packages/backend-tfjs/test/ppo-bootstrap.test.ts b/packages/backend-tfjs/test/ppo-bootstrap.test.ts
new file mode 100644
index 0000000..f636b76
--- /dev/null
+++ b/packages/backend-tfjs/test/ppo-bootstrap.test.ts
@@ -0,0 +1,93 @@
+import { expect, it } from 'vitest';
+import * as tf from '@tensorflow/tfjs';
+import { PPOAgent } from '../src/agents/ppo';
+import type { ModelStorageProvider } from '@ignitionai/storage';
+
+// A persisted policy with equal logits and a critic predicting V(s)=2.
+function storage(): ModelStorageProvider {
+  return {
+    async load(id) {
+      const net = tf.sequential();
+      net.add(tf.layers.dense({ inputShape: [1], units: id.endsWith('/actor') ? 2 : 1,
+        kernelInitializer: 'zeros', biasInitializer: tf.initializers.constant({ value: id.endsWith('/actor') ? 0 : 2 }) }));
+      return net;
+    },
+    async save() { return 'memory'; }, async list() { return []; },
+    async delete() {}, async exists() { return true; },
+  };
+}
+
+it.each([
+  { terminated: false, truncated: false, expected: 0 },
+  { terminated: false, truncated: true, expected: 0 },
+  { terminated: true, truncated: false, expected: 1 },
+])('single-transition update bootstraps only nonterminal states: %j', async ({ terminated, truncated, expected }) => {
+  const agent = new PPOAgent({ inputSize: 1, actionSize: 2, hiddenLayers: [8],
+    gamma: 0.9, epochs: 1, entropyCoef: 0, storageProvider: storage() });
+  try {
+    await agent.load('fixture');
+    const action = await agent.getAction([0], true); // tie chooses 0
+    agent.remember({ state: [0], nextState: [0], action, reward: 0.5, terminated, truncated });
+    // Nonterminal advantage: .5 + .9*2 - 2 = .3; terminal: .5 - 2 = -1.5.
+    await agent.train();
+    expect(await agent.getAction([0], true)).toBe(expected);
+  } finally { agent.dispose(); }
+});
+
+it.each([false, true])('GAE does not carry rewards across an episode boundary (truncated=%s)', async (truncated) => {
+  let firstValue = 0;
+  const provider = storage();
+  provider.load = async (id) => {
+    const net = tf.sequential();
+    net.add(tf.layers.dense({ inputShape: [2], units: id.endsWith('/actor') ? 2 : 1,
+      useBias: false, kernelInitializer: tf.initializers.constant({ value: id.endsWith('/actor') ? 0 : 2 }) }));
+    return net;
+  };
+  provider.save = async (id, model) => {
+    if (id.endsWith('/critic')) firstValue = tf.tidy(() => (model.predict(tf.tensor2d([[1, 0]])) as tf.Tensor).dataSync()[0]);
+    return 'memory';
+  };
+  const agent = new PPOAgent({ inputSize: 2, actionSize: 2, hiddenLayers: [8],
+    gamma: 0.9, gaeLambda: 1, epochs: 1, batchSize: 2, storageProvider: provider });
+  try {
+    await agent.load('fixture');
+    const action = await agent.getAction([1, 0], true);
+    agent.remember({ state: [1, 0], nextState: [1, 0], action, reward: 3,
+      terminated: !truncated, truncated });
+    const nextAction = await agent.getAction([0, 1], true);
+    agent.remember({ state: [0, 1], nextState: [0, 1], action: nextAction, reward: -100,
+      terminated: true, truncated: false });
+    await agent.train();
+    await agent.save('result');
+    // First target is 3 (terminal) or 4.8 (truncated), both above V=2.
+    // Leaking the next episode's -100 reward would instead decrease it.
+    expect(firstValue).toBeGreaterThan(2);
+  } finally { agent.dispose(); }
+});
+
+it('bootstraps the actual successor rather than the current observation', async () => {
+  const provider = storage();
+  let probability = 0.5;
+  provider.load = async (id) => {
+    const net = tf.sequential();
+    net.add(tf.layers.dense({ inputShape: [1], units: id.endsWith('/actor') ? 2 : 1,
+      kernelInitializer: tf.initializers.constant({ value: id.endsWith('/actor') ? 0 : 2 }),
+      biasInitializer: 'zeros' }));
+    return net;
+  };
+  provider.save = async (id, model) => {
+    if (id.endsWith('/actor')) probability = tf.tidy(() => tf.softmax(model.predict(tf.tensor2d([[0]])) as tf.Tensor).dataSync()[0]);
+    return 'memory';
+  };
+  const agent = new PPOAgent({ inputSize: 1, actionSize: 2, hiddenLayers: [8], gamma: 0.9,
+    epochs: 1, entropyCoef: 0, storageProvider: provider });
+  try {
+    await agent.load('fixture');
+    const action = await agent.getAction([0], true);
+    agent.remember({ state: [0], nextState: [1], action, reward: -0.5, terminated: false, truncated: false });
+    await agent.train();
+    await agent.save('result');
+    // -.5 + .9*V(1) - V(0) = 1.3. Using V(0) gives -.5 instead.
+    expect(probability).toBeGreaterThan(0.5);
+  } finally { agent.dispose(); }
+});
diff --git a/packages/backend-tfjs/test/ppo-public.test.ts b/packages/backend-tfjs/test/ppo-public.test.ts
new file mode 100644
index 0000000..033df38
--- /dev/null
+++ b/packages/backend-tfjs/test/ppo-public.test.ts
@@ -0,0 +1,44 @@
+import { expect, it, vi } from 'vitest';
+import * as tf from '@tensorflow/tfjs';
+import { IgnitionEnvTFJS } from '../src/ignition-env-tfjs';
+
+it.each([11, 29, 47])('PPO learns rewarded actions through the public loop (seed %i)', async (seed) => {
+  await tf.setBackend('cpu');
+  let rng = seed;
+  const random = vi.spyOn(Math, 'random').mockImplementation(() => {
+    rng = (Math.imul(rng, 1664525) + 1013904223) >>> 0;
+    return rng / 4294967296;
+  });
+  let action = 0;
+  const env = new IgnitionEnvTFJS({
+    actions: 2, observe: () => [0], step: (a: number | number[]) => { action = a as number; },
+    reward: () => action === 1 ? 1 : -1, done: () => true, reset: () => {},
+  });
+  try {
+    env.train('ppo', { hiddenLayers: [8], lr: 0.01, entropyCoef: 0, batchSize: 32 });
+    env.stop();
+    for (let i = 0; i < 512; i++) await env.step();
+    let correct = 0;
+    for (let i = 0; i < 200; i++) correct += (await env.agent!.getAction([0])) as number;
+    expect(correct / 200).toBeGreaterThan(0.8);
+  } finally {
+    env.stop();
+    env.agent?.dispose?.();
+    random.mockRestore();
+  }
+}, 30000);
+
+it.each(['reset', 'inferStep'] as const)('discards a partial rollout on %s', async (operation) => {
+  const env = new IgnitionEnvTFJS({ actions: 2, observe: () => [0], step: () => {},
+    reward: () => 1, done: () => false, reset: () => {} });
+  try {
+    env.train('ppo', { hiddenLayers: [8], rolloutSize: 2 });
+    env.stop();
+    await env.step();
+    await env[operation]();
+    await env.step();
+    expect(env.agent!.getState!().trainStepCounter).toBe(0);
+    await env.step();
+    expect(env.agent!.getState!().trainStepCounter).toBe(1);
+  } finally { env.stop(); env.agent?.dispose?.(); }
+});
diff --git a/packages/backend-tfjs/test/qtable-truncation.test.ts b/packages/backend-tfjs/test/qtable-truncation.test.ts
new file mode 100644
index 0000000..26f9de4
--- /dev/null
+++ b/packages/backend-tfjs/test/qtable-truncation.test.ts
@@ -0,0 +1,13 @@
+import { it, expect } from 'vitest';
+import { QTableAgent } from '../src/agents/qtable';
+
+it('Q-table bootstraps a truncated transition from its actual successor', async () => {
+  const agent = new QTableAgent({ inputSize: 1, actionSize: 2, lr: 0.9, gamma: 0.9, epsilon: 0 });
+  agent.remember({ state: [1], action: 0, reward: 2, nextState: [1], terminated: true, truncated: false });
+  await agent.train();
+  agent.remember({ state: [0], action: 0, reward: 0.5, nextState: [0], terminated: true, truncated: false });
+  await agent.train();
+  agent.remember({ state: [0], action: 1, reward: 0, nextState: [1], terminated: false, truncated: true });
+  await agent.train();
+  expect(await agent.getAction([0], true)).toBe(1);
+});
diff --git a/packages/core/src/env-validation.ts b/packages/core/src/env-validation.ts
index eb5d2fb..78819e2 100644
--- a/packages/core/src/env-validation.ts
+++ b/packages/core/src/env-validation.ts
@@ -18,6 +18,9 @@ export function validateTrainingEnv(env: unknown): asserts env is TrainingEnv {
   assertFn(e, 'reward', 'TrainingEnv');
   assertFn(e, 'done', 'TrainingEnv');
   assertFn(e, 'reset', 'TrainingEnv');
+  for (const name of ['terminated', 'truncated']) {
+    if (e[name] !== undefined) assertFn(e, name, 'TrainingEnv');
+  }
 
   if (e.actions === undefined || e.actions === null) {
     throw new Error('[IgnitionAI] TrainingEnv must have an actions property (string[] or number)');
diff --git a/packages/core/src/ignition-env.ts b/packages/core/src/ignition-env.ts
index 48c83af..a45e989 100644
--- a/packages/core/src/ignition-env.ts
+++ b/packages/core/src/ignition-env.ts
@@ -80,7 +80,8 @@ export class IgnitionEnv {
 
     const observation = this.env.observe();
     const reward = this.env.reward();
-    const terminated = this.env.done();
+    const truncated = this.env.truncated?.() ?? false;
+    const terminated = this.env.terminated?.() ?? (this.env.done() && !truncated);
 
     const experience: Experience = {
       state: this.currentState,
@@ -88,15 +89,15 @@ export class IgnitionEnv {
       reward,
       nextState: observation,
       terminated,
-      truncated: false,
+      truncated,
     };
 
     this._agent.remember(experience);
-    await this._agent.train();
+    if (this._agent.shouldTrain?.() ?? true) await this._agent.train();
 
-    const result: StepResult = { observation, reward, terminated, truncated: false };
+    const result: StepResult = { observation, reward, terminated, truncated };
 
-    if (terminated) {
+    if (terminated || truncated) {
       this.env.reset();
       this.currentState = this.env.observe();
     } else {
@@ -113,16 +114,18 @@ export class IgnitionEnv {
 
     this.stepCount++;
 
+    this._agent.discardRollout?.();
     const action = await this._agent.getAction(this.currentState, true);
     this.env.step(action);
 
     const observation = this.env.observe();
     const reward = this.env.reward();
-    const terminated = this.env.done();
+    const truncated = this.env.truncated?.() ?? false;
+    const terminated = this.env.terminated?.() ?? (this.env.done() && !truncated);
 
-    const result: StepResult = { observation, reward, terminated, truncated: false };
+    const result: StepResult = { observation, reward, terminated, truncated };
 
-    if (terminated) {
+    if (terminated || truncated) {
       this.env.reset();
       this.currentState = this.env.observe();
     } else {
@@ -173,6 +176,7 @@ export class IgnitionEnv {
   }
 
   public reset(): void {
+    this._agent?.discardRollout?.();
     this.env.reset();
     this.currentState = this.env.observe();
     this.stepCount = 0;
diff --git a/packages/core/src/types.ts b/packages/core/src/types.ts
index 701f99f..d739e31 100644
--- a/packages/core/src/types.ts
+++ b/packages/core/src/types.ts
@@ -54,6 +54,10 @@ export interface AgentInterface {
   getAction(observation: number[], greedy?: boolean): Promise<number | number[]>;
   remember(experience: Experience): void;
   train(): Promise<void>;
+  /** Whether the collected transitions are ready for an automatic update. */
+  shouldTrain?(): boolean;
+  /** Discard on-policy data when execution leaves the training trajectory. */
+  discardRollout?(): void;
   /** Release TF/GPU/WASM resources held by the agent */
   dispose?(): void;
   /** Reset agent internal state (epsilon, memory, counters…) */
@@ -99,6 +103,10 @@ export interface TrainingEnv {
   reward(): number;
   /** Return true if the episode is over */
   done(): boolean;
+  /** Optional explicit terminal condition; overrides legacy done(). */
+  terminated?(): boolean;
+  /** Optional external episode limit (default: false). */
+  truncated?(): boolean;
   /** Reset the environment for a new episode */
   reset(): void;
 }
@@ -115,7 +123,7 @@ export interface InferenceEnv {
 
 // ─── Auto-configuration ─────────────────────────────────────────────────────
 
-export type AlgorithmType = 'dqn' | 'ppo' | 'qtable';
+export type AlgorithmType = 'dqn' | 'double-dqn' | 'ppo' | 'qtable';
 
 /**
  * Factory function that creates an agent from a merged config.
diff --git a/packages/core/test/episode-end.test.ts b/packages/core/test/episode-end.test.ts
new file mode 100644
index 0000000..c831461
--- /dev/null
+++ b/packages/core/test/episode-end.test.ts
@@ -0,0 +1,39 @@
+import { expect, it, vi } from 'vitest';
+import { IgnitionEnv } from '../src/ignition-env';
+
+it.each(['step', 'inferStep'] as const)('%s reports truncation and resets without losing the last observation', async (method) => {
+  let position = 0;
+  const world = {
+    actions: 2, observe: () => [position], step: () => { position = 5; },
+    reward: () => 1, done: () => true, truncated: () => true,
+    reset: () => { position = 0; },
+  };
+  const loop = new IgnitionEnv(world);
+  const remember = vi.fn();
+  loop.agent = { getAction: async () => 0, remember, train: async () => {} };
+  expect(await loop[method]()).toEqual({ observation: [5], reward: 1, terminated: false, truncated: true });
+  expect(position).toBe(0);
+  if (method === 'step') expect(remember).toHaveBeenCalledWith(expect.objectContaining({ nextState: [5], terminated: false, truncated: true }));
+});
+
+it.each([
+  { explicit: undefined, truncated: undefined, terminal: true, cut: false },
+  { explicit: true, truncated: true, terminal: true, cut: true },
+  { explicit: false, truncated: false, terminal: false, cut: false },
+])('preserves legacy done and explicit terminal precedence: %j', async ({ explicit, truncated, terminal, cut }) => {
+  const reset = vi.fn();
+  const loop = new IgnitionEnv({ actions: 2, observe: () => [0], step: () => {},
+    reward: () => 0, done: () => true, reset,
+    ...(explicit === undefined ? {} : { terminated: () => explicit }),
+    ...(truncated === undefined ? {} : { truncated: () => truncated }),
+  });
+  loop.agent = { getAction: async () => 0, remember: () => {}, train: async () => {} };
+  expect(await loop.step()).toMatchObject({ terminated: terminal, truncated: cut });
+  expect(reset).toHaveBeenCalledTimes(terminal || cut ? 1 : 0);
+});
+
+it.each(['terminated', 'truncated'])('rejects a non-function %s callback at construction', (name) => {
+  expect(() => new IgnitionEnv({ actions: 2, observe: () => [0], step: () => {},
+    reward: () => 0, done: () => false, reset: () => {}, [name]: true,
+  })).toThrow(new RegExp(name));
+});
diff --git a/packages/demo-car-circuit/README.md b/packages/demo-car-circuit/README.md
new file mode 100644
index 0000000..3790a9b
--- /dev/null
+++ b/packages/demo-car-circuit/README.md
@@ -0,0 +1,46 @@
+# Circuit Racing
+
+Driving/race V2 add boundary collisions and immobilization rescue. The bundled imitation checkpoints were trained afresh against these rules (learning V2). Old checkpoints are rejected instead of silently relabelled. The Q V3 comparison and all 200 evaluations are available in the linked report. Older checkpoints and results remain historical.
+
+Run `pnpm --filter demo-car-circuit dev` from the workspace root, then open the displayed local URL. Run tests from the root with `pnpm exec vitest run packages/demo-car-circuit/test` (the Vite root is `src`).
+
+The first racing slice provides two imported Kenney Car Kit vehicles (CC0), Alpine Park, a following camera and deterministic arcade driving at 60 simulation ticks per second. Accelerate with arrows/WASD/ZQSD, brake with Down/S, steer with Left/Right or A/Q/D. Escape, losing focus and changing vehicle pause the session. Both vehicles share identical physics.
+
+Asset license and provenance are in `src/public/models/`. The versioned nine-action driving contract is in `src/racing/driving.ts`. The legacy constant-speed environment and its evaluation tests remain available; its checkpoints are not compatible with the racing contract.
+
+Course mode adds a three-second countdown, three laps through 20 ordered checkpoints, standings and results. Up to four cars use a shared decision batch and physics clock. Ordered gates reject skipped checkpoints; brief cuts between gates are penalized rather than physically forbidden. Off-road excursions cost two seconds. Remaining within one metre for five seconds, on or off the road, rescues the vehicle to its last validated checkpoint and adds five seconds. Moving off-road does not trigger a rescue. Continuous rails at seven metres from the centreline contain the shared 1.2-metre collision radius, removing outward impact speed. Car contacts separate their collision bodies and reduce speed. A race stops at five simulated minutes or when all drivers finish. Equal times are ordered by stable driver ID.
+
+The observation mode explicitly uses four **rule-based reference controllers**, not learned policies. The Train mode now creates a real imitation MLP, aggregates trajectories on Alpine Park, and switches from solo to frozen reference traffic after eight rounds. A new run uses 16 rounds of 4,096 examples and three Adam epochs per round (learning rate 0.003, batch size 128, two 32-unit tanh layers, nine-way softmax). The seed fixes initialization and sampling; the 20-element observation contains driving state, track anticipation and up to three opponents.
+
+Stop completes the current gradient update then stops collection. Resume uses the loaded driver's seed and weights; loading recreates the optimizer, so this is a weights resume rather than an exact optimizer-state continuation. New driver discards the current unsaved network. Save stores real weights, contracts and reports in this browser's local garage. Reload refuses incompatible contracts or malformed weights.
+
+Evaluation reconstructs a separate frozen network and checks both circuits, solo and with three rule-based traffic vehicles, using scenario seeds 101/307/509. Success means three laps, no rescue and no more than ten penalty seconds. Results remain attached to the evaluated weights; resumed training clears old reports.
+
+`src/public/drivers/` contains all three scheduled training seeds and their complete reports, including failures. Aggregate measured success is 33/36 (9/12, 12/12 and 12/12 for seeds 11, 29 and 47); no checkpoint was selected by test-track performance. The three failures are seed 11 on the held-out circuit without traffic. These imitation drivers are imperfect and often make contact in traffic. The pre-run protocol and source hashes are retained in `src/public/drivers/protocol.json`; the former checkpoints are archived in `src/public/reports/imitation-v1/`. Run `pnpm exec tsx --tsconfig scripts/tsconfig.racing.json scripts/train-racing.mts` at the root to reproduce native training/evaluation (requires the installed TFJS Node addon).
+
+Course now opens a garage for two to four learned competitors. Select a local saved checkpoint or any of the three supplied trained networks, choose each vehicle independently, and start the AI race. The camera selector follows each driver's own speed, lap and penalty metrics. Results identify each checkpoint. Each race owns separately loaded frozen networks and disposes them on session changes. Missing bundled files are reported without hiding available local drivers; no rule-based substitute is used.
+
+Race V2 browser proof: new checkpoints 29 and 11 completed three laps in 103.27 s and 115.40 s, with zero penalties and rescues. The lap counter remains visible in narrow desktop windows and the result list separates checkpoint identity from finish time. The public integration test verifies completion with unchanged weights. In the garage, choose Play against AI to drive with one to three learned opponents. Choose your vehicle independently; arrows/WASD/ZQSD use the same nine-action physics as the networks. Opening garage controls pauses the race. The HUD adds position, laps, penalties and a live map; the camera remains on the player.
+
+Current player verification: shared-clock acceleration/braking and frozen-opponent tests pass; browser acceleration and Escape pause were observed. A browser run completing all three human laps remains unverified and is not claimed by the AI-only race proof.
+
+The frozen racing evaluation protocol names Alpine Park for training, Harbour for held-out testing, and seeds 101/307/509 before learned checkpoint selection. The original constant-speed evaluation has its separate version.
+
+
+## DQN and Double DQN on the racing contract
+
+The method selector creates imitation, DQN or Double DQN drivers. Q-learning uses the same twenty observations, nine actions and arcade physics, with one decision every six ticks in training, evaluation and races. Q checkpoints use ReLU hidden layers and linear outputs; imitation retains tanh and softmax. The checkpoint algorithm and protocol identify the architecture and decision cadence, and all three methods can share a learned race.
+
+`racing-q-learning-v3` fixes 20,000 training decisions, five training seeds, and twenty frozen full-race evaluations per checkpoint (five evaluation seeds × both tracks × solo/traffic). Training starts at varied positions on Alpine Park, uses10,000 solo decisions then10,000 with frozen reference traffic, preserves off-road recovery and uses progress rewards; evaluation uses the full standing-start race. Resume recreates optimizer, replay and exploration from the loaded weights, rather than restoring exact training state.
+
+Native V3 results: DQN 42/100 successes (61 completed), Double DQN 33/100 (77 completed). Success rates across five training seeds ranged 0–85% and 0–80%; population standard deviations 38.0 and 33.0 percentage points. Double DQN finished more races but met the predeclared penalty/rescue criterion less often. Raw failures, all final checkpoints, timing, versions and source hashes are in `src/public/reports/racing-q-v3/` and its rendered report. Native and browser CPU runs use different numeric backends and need not produce identical trained weights or scores.
+
+V1 remains a historical archive: it stopped recoverable off-road excursions and trained solo only. V2 corrected that curriculum but still used race V1 without barrier collisions and with off-road-duration rescue. Neither archive validates the corrected race rules.
+
+Reproduce with `pnpm exec tsx --tsconfig scripts/tsconfig.racing.json scripts/compare-racing-q.mts`, then `node scripts/report-racing-q.mjs`. The source aliases match the demo's Vite aliases; the repository's existing package ESM export points at an unbuilt file, so the runner does not rely on that package export. The runner refuses to overwrite an existing protocol file. Preserve or move the existing `.scratch/circuit-racing/q-comparison-v3` directory before a fresh run.
+
+Browser CPU V3 verification: a newly trained Double DQN seed 11 checkpoint (`driver-11-1788717838571`) survived save → page reload → load and completed all twenty evaluations, with 15/20 successes. The evaluated copy was saved as `driver-11-1788718008363`. Old race-V1/Q-V2 checkpoints are explicitly rejected as incompatible. This checks the browser workflow independently of the native benchmark. The mixed browser race also completed: Double DQN checkpoint `driver-11-1788718008363` finished in 222.15 s and supplied imitation 29 in 240.77 s. Both finished three laps; this remains an AI-only race, not the outstanding human acceptance test.
+
+The training interface guides users through Train → Test → Save → Race with one primary action at each step. Technical configuration and individual evaluation results stay in expandable details. The 3D view is explicitly labelled as a session snapshot, not continuous racing. Saving carries the exact checkpoint into the garage; switching to player mode preserves that opponent.
+
+Interactive races now use **ghost mode** (`circuit-race-v2-ghost-v1`): cars cannot push or slow each other; rail collisions, off-road penalties and rescue still apply. This includes AI races and player opponents. Existing checkpoints can race in this mode, while their recorded training/evaluation scores remain explicitly labelled as contact-mode results. The default training/evaluation simulation keeps its original contact rules so those protocols are not silently changed.
diff --git a/packages/demo-car-circuit/VALIDATION.md b/packages/demo-car-circuit/VALIDATION.md
new file mode 100644
index 0000000..ea7431d
--- /dev/null
+++ b/packages/demo-car-circuit/VALIDATION.md
@@ -0,0 +1,55 @@
+# Local validation — 6 September 2026
+
+Status: implementation and automated/local-browser checks delivered; **a full human-driven three-lap race against a named learned checkpoint remains unverified**. No push, merge, deployment or issue closure is implied.
+
+## Scope and evidence
+
+| Scope | Evidence | Boundary |
+| --- | --- | --- |
+| #7 PPO public learning | Rollout/termination/truncation tests and public learning across seeds 11, 29, 47; native PPO convergence suite passes. | Three live Hugging Face tests skip without credentials. |
+| #8 Frozen legacy Circuit evaluation | Distinct training/test tracks, controlled starts, explicit limits, versioned reports and public integration tests. | Legacy constant-speed checkpoints remain incompatible with Racing. |
+| #10 Catalogue | One JSON catalogue drives eight routes, homepage counts and docs; all eight built with correct base paths. Production gallery and docs checked. | Target Chasing has not been accepted into this catalogue. |
+| #13 3D driving | Two imported Kenney CC0 GLBs with texture, licence and source hashes; common fixed 60 Hz physics, keyboard/focus/pause tests and browser rendering. Asset failure/Retry previously verified; final rails and assets rendered in production localhost. | Arcade desktop physics with circular collision bodies; local measurements are not internet latency promises. |
+| #14 Race rules | Race V2: ordered gates, three laps, standings/ties, car contacts, continuous rail contacts, penalties, immobilization rescue and common decision clock. Combined car/rail constraints and finish/rescue ordering have regression tests. Four rule references finish in public simulation tests. | Off-road costs 2 s. Staying within 1 m for 5 s triggers rescue (+5 s); moving excursions remain recoverable. |
+| #15 Real driver persistence | Fresh imitation V2 seeds 11, 29, 47: 33/36 successes, all checkpoints/reports retained. Pre-run protocol and source hashes match commit `020ba09`. Browser Q V3 training → save → full reload → load → frozen evaluation also completed. | Seed 11 fails the three held-out solo scenarios. Frequent traffic contacts remain. Weights resume recreates optimizer. |
+| #16 Learned races | Fresh imitation V2 checkpoints 29 and 11 completed three browser laps in 103.27 s and 115.40 s, zero penalties/rescues. Separate frozen copies and unchanged weights tested. | No rule-controller fallback. This is not a human race proof. |
+| #11 Double DQN | Public analytic online/target test, API train/save/infer, same-seed weights, repeated-load memory test, selector and Q checkpoint reload. Browser CPU Double DQN seed 11 trained 20,000 transitions and scored 15/20 after save/full reload/load. Old race-V1 checkpoints show an incompatibility error. | Browser CPU and native TensorFlow are different numeric backends. Mixed browser race completed: Q checkpoint `driver-11-1788718008363` in 222.15 s, imitation `bundled-29` in 240.77 s. |
+| #12 Equal-budget comparison | [V3 report](src/public/reports/racing-q-v3/index.html): five seeds × twenty full evaluations × two methods, 20,000 transitions and 4,993 updates each. All failures, weights, timings, versions and source hashes retained; two independent reviews verified the artifacts. | DQN 42/100 successes, 61 finishes; Double DQN 33/100, 77 finishes. No gain on aggregate success. V1/V2 are historical archives. |
+| #17 Player versus trained AI | Two vehicle choices, one to three trained opponents, common physics/rules, HUD/map, pause/results. Public acceleration/braking/shared-clock/frozen-opponent tests pass. Browser short-key acceleration/pause previously verified. | **A complete three-lap human keyboard race against a named learned checkpoint is still unverified.** |
+
+## V3 experiment contract
+
+`racing-q-learning-v3`, driving `circuit-racing-v2`, race `circuit-race-v2`: 10,000 solo decisions, then 10,000 with three frozen rule-reference traffic drivers. Six common physics ticks per Q decision in training, evaluation and races. Boundaries, collisions, immobilization rescue and off-road penalties are shared. Episodes truncate after 600 decisions or at the curriculum boundary; only finishing terminates them. Last scheduled weights are retained without evaluation-based selection.
+
+Training seeds: 11, 29, 47, 73, 101. Evaluation seeds: 211, 307, 419, 509, 601 on Alpine Park and Harbour, solo and traffic. Success requires three laps, no rescue and at most ten penalty seconds. Evaluation seeds vary the initial heading narrowly; these are not independent broad driving scenarios.
+
+DQN success across training seeds: 0–85%, population standard deviation 38.0 percentage points. Double DQN: 0–80%, 33.0 points. More Double DQN finishes do not imply more successes because penalties/rescues count. The result does not demonstrate general algorithm superiority.
+
+V1 stopped recoverable off-road excursions and omitted training traffic. V2 corrected those two problems but used race V1 without boundary collision and rescued moving cars after five seconds off-road. Their raw data remains preserved; neither supports acceptance of the corrected rules.
+
+Browser CPU V3: checkpoint `driver-11-1788717838571` was saved after training, restored after full page reload and evaluated on all twenty scenarios (15/20 success). Evaluated copy: `driver-11-1788718008363`. Browser and native training are distinct runs on different numeric backends. The evaluated browser Q checkpoint then completed three laps against imitation 29: 222.15 s versus 240.77 s; both arrivals and checkpoint IDs were visible in the results.
+
+## Runtime measurements
+
+Final production localhost, Apple M4 / 16 GiB, Chromium 152 on macOS, 1280 × 720, four fresh imitation networks (11, 29, 47, 11): 180 animation-frame intervals, median **8.3 ms**, p95 **9.5 ms**, mean **8.331 ms**. Active four-car traffic, approximately 44 s into the race. Warm local rendering; not a universal hardware guarantee. Formula R / Sport GT, Alpine Park, driving/race V2 and observations V1.
+
+With browser cache disabled, final local Formula GLB transfer took **16.2 ms** (26,667 compressed body bytes), finishing 190.6 ms after navigation; texture **10.5 ms**, finishing 242.6 ms. Source GLB size: 167,272 bytes. Cache restored and Network instrumentation disabled afterward. Earlier asset-error validation blocked the GLB, observed “Le véhicule n’a pas pu être chargé.”, restored access and recovered via Retry; the loader/error boundary is unchanged by the rule correction.
+
+Final rendered V3 report opened through the Training link. Report, raw JSON and all ten weight files returned HTTP 200; raw JSON contains all 200 evaluations. Narrow-window lap counter and readable result rows checked visually.
+
+## Commands and remaining manual check
+
+- `npm test`: full workspace build, static demo build, Next build and **343 passing tests**, three credential-dependent integration skips.
+- `pnpm run typecheck`: passed.
+- `npm run lint`: exits successfully but explicitly skips ESLint because of existing configuration incompatibility. This is **not lint validation**.
+- All eight production demo HTML files reference their correct `/demos/<slug>/assets/` base.
+
+To complete #17: open local Circuit, choose Course → Play against AI, Sport GT and supplied imitation V2 checkpoint 29, then finish all three laps. Record checkpoint ID, result, penalties and replay/pause behavior. The available CUA key API emits short presses and has no key-hold operation. Automatic approval review rejected direct CDP key input in favour of CUA. The required human race has not been substituted with scripted AI driving.
+
+## Training UX follow-up
+
+The reported difficulty was clarified as an unclear workflow, not a failed training computation. Reproduced the old interface: “Nouveau pilote” trained 16 sessions but returned to “Prêt”, without a clear next action or snapshot explanation. The revised guided flow was exercised in the browser: train → 9/12 evaluated → save → garage checkpoint `driver-11-1788719094256` → player mode with the same checkpoint still selected. Existing training algorithms and protocols are unchanged. Circuit TypeScript checking, 61 Circuit tests and production build passed; the new entry screen was visually checked on localhost3030. The test driver in that origin was saved before refreshing.
+
+## Ghost-mode change requested by the maintainer
+
+The maintainer explicitly requested no collisions between cars after observing a pile-up. Interactive AI/player/reference races now use `circuit-race-v2-ghost-v1`; this supersedes the earlier car-contact requirement for those races. Barriers, gates, off-road penalties and immobilization rescue remain active. Training/evaluation retain contact rules; the garage labels their recorded scores accordingly. Previous race results above describe the earlier contact mode, not ghost-mode performance. Regression tests verify overlapping cars follow exactly the solo trajectory without speed loss, barriers still constrain them, and both learned AI/human races enable ghosts. All 65 Circuit tests, Circuit TypeScript checking and the production build pass. The new ghost label and four-car start were checked in the final localhost3030 build.
diff --git a/packages/demo-car-circuit/package.json b/packages/demo-car-circuit/package.json
index a196086..c670b37 100644
--- a/packages/demo-car-circuit/package.json
+++ b/packages/demo-car-circuit/package.json
@@ -3,16 +3,21 @@
   "private": true,
   "version": "0.0.0",
   "type": "module",
-  "scripts": { "dev": "vite", "build": "vite build", "test": "vitest run" },
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "test": "vitest run"
+  },
   "dependencies": {
     "@ignitionai/backend-tfjs": "workspace:*",
     "@ignitionai/core": "workspace:*",
     "@react-three/drei": "^10.0.6",
     "@react-three/fiber": "^9.1.2",
-    "three": "^0.162.0",
+    "@tensorflow/tfjs": "4.22.0",
     "react": "^19.0.0",
     "react-dom": "^19.0.0",
     "recharts": "^2.15.3",
+    "three": "^0.162.0",
     "zustand": "^5.0.3"
   },
   "devDependencies": {
diff --git a/packages/demo-car-circuit/src/circuit-env.ts b/packages/demo-car-circuit/src/circuit-env.ts
index a512287..676ee9a 100644
--- a/packages/demo-car-circuit/src/circuit-env.ts
+++ b/packages/demo-car-circuit/src/circuit-env.ts
@@ -4,6 +4,22 @@ import { OvalTrack } from './track';
 const SPEED = 0.15;
 const STEER_DELTA = 0.08; // radians per step
 const MAX_STEPS = 500;
+export const SIMULATION_STEP_SECONDS = 0.05;
+
+export interface CircuitOptions {
+  maxSteps?: number;
+  targetLaps?: number;
+  startWaypoint?: number;
+}
+
+export interface CircuitEpisode {
+  transitions: number;
+  completedLaps: number;
+  lapTimesSeconds: number[];
+  simulationSeconds: number;
+  endReason: 'success' | 'off-track' | 'time-limit';
+}
+
 const LAP_BONUS = 50;
 const OFF_TRACK_PENALTY = -10;
 const LAPS_TO_WIN = 3;
@@ -23,6 +39,14 @@ export class CircuitEnv implements TrainingEnv {
   stepCount = 0;
   laps = 0;
   private lastProgress = 0;
+  private readonly maxSteps: number;
+  private readonly targetLaps: number;
+  private readonly startWaypoint: number;
+  private forwardProgress = 0;
+  private lastLapStep = 0;
+  private lapTimesSeconds: number[] = [];
+  /** Most recent completed episode; retained across automatic resets. */
+  lastEpisode: CircuitEpisode | null = null;
   private offTrack = false;
   private justCompletedLap = false;
 
@@ -31,13 +55,22 @@ export class CircuitEnv implements TrainingEnv {
   private alignment = 0;
   private lateralNorm = 0;
 
-  constructor(straightLen = 10, radius = 4, halfWidth = 2) {
+  constructor(straightLen = 10, radius = 4, halfWidth = 2, options: CircuitOptions = {}) {
     this.track = new OvalTrack(straightLen, radius, halfWidth);
-    // Start on top straight, facing right
-    const start = this.track.waypoints[0];
+    this.maxSteps = options.maxSteps ?? MAX_STEPS;
+    this.targetLaps = options.targetLaps ?? LAPS_TO_WIN;
+    this.startWaypoint = options.startWaypoint ?? 0;
+    if (!Number.isInteger(this.maxSteps) || this.maxSteps < 1 ||
+        !Number.isInteger(this.targetLaps) || this.targetLaps < 1 ||
+        !Number.isInteger(this.startWaypoint) || this.startWaypoint < 0 ||
+        this.startWaypoint >= this.track.waypoints.length - 1) {
+      throw new Error('Invalid Circuit episode options');
+    }
+    const start = this.track.waypoints[this.startWaypoint];
     this.carX = start.x;
     this.carY = start.y;
-    this.carAngle = 0;
+    this.carAngle = this.track.nearestPoint(start.x, start.y).trackAngle;
+    this.lastProgress = this.track.nearestPoint(start.x, start.y).progress;
   }
 
   observe(): number[] {
@@ -87,9 +120,12 @@ export class CircuitEnv implements TrainingEnv {
 
     // Lap completion — fire bonus only once per crossing
     this.justCompletedLap = false;
-    if (result.progress < 0.1 && this.lastProgress > 0.9) {
+    this.forwardProgress += delta;
+    if (!this.offTrack && this.forwardProgress >= this.laps + 1 - 1e-9) {
       this.laps++;
       this.justCompletedLap = true;
+      this.lapTimesSeconds.push((this.stepCount + 1 - this.lastLapStep) * SIMULATION_STEP_SECONDS);
+      this.lastLapStep = this.stepCount + 1;
     }
     this.lastProgress = result.progress;
 
@@ -110,20 +146,37 @@ export class CircuitEnv implements TrainingEnv {
     return this.justCompletedLap ? base + LAP_BONUS : base;
   }
 
+  terminated(): boolean {
+    return this.offTrack || this.laps >= this.targetLaps;
+  }
+
+  truncated(): boolean {
+    return !this.terminated() && this.stepCount >= this.maxSteps;
+  }
+
   done(): boolean {
-    if (this.offTrack) return true;
-    if (this.laps >= LAPS_TO_WIN) return true;
-    return this.stepCount >= MAX_STEPS;
+    return this.terminated() || this.truncated();
   }
 
   reset(): void {
-    const start = this.track.waypoints[0];
+    if (this.done()) {
+      this.lastEpisode = {
+        transitions: this.stepCount, completedLaps: this.laps,
+        lapTimesSeconds: [...this.lapTimesSeconds],
+        simulationSeconds: this.stepCount * SIMULATION_STEP_SECONDS,
+        endReason: this.offTrack ? 'off-track' : this.laps >= this.targetLaps ? 'success' : 'time-limit',
+      };
+    }
+    const start = this.track.waypoints[this.startWaypoint];
     this.carX = start.x;
     this.carY = start.y;
-    this.carAngle = 0;
+    this.carAngle = this.track.nearestPoint(start.x, start.y).trackAngle;
     this.stepCount = 0;
     this.laps = 0;
-    this.lastProgress = 0;
+    this.lastProgress = this.track.nearestPoint(start.x, start.y).progress;
+    this.forwardProgress = 0;
+    this.lastLapStep = 0;
+    this.lapTimesSeconds = [];
     this.offTrack = false;
     this.justCompletedLap = false;
     this.progressDelta = 0;
diff --git a/packages/demo-car-circuit/src/evaluation.ts b/packages/demo-car-circuit/src/evaluation.ts
new file mode 100644
index 0000000..231d94d
--- /dev/null
+++ b/packages/demo-car-circuit/src/evaluation.ts
@@ -0,0 +1,74 @@
+import { IgnitionEnv, type AgentInterface } from '@ignitionai/core';
+import { CircuitEnv, SIMULATION_STEP_SECONDS, type CircuitEpisode } from './circuit-env';
+
+/** Fixed before checkpoint selection. Changing any setting requires a new id. */
+export const CIRCUIT_PROTOCOL = Object.freeze({
+  id: 'circuit-evaluation-v1',
+  circuits: Object.freeze({
+    training: Object.freeze({ straightLength: 10, radius: 4, halfWidth: 2 }),
+    test: Object.freeze({ straightLength: 6, radius: 6, halfWidth: 2 }),
+  }),
+  startWaypoints: Object.freeze([0, 16, 32]),
+  maxSteps: 1500,
+  targetLaps: 3,
+  stepSeconds: SIMULATION_STEP_SECONDS,
+});
+
+export interface CircuitEvaluationReport {
+  schemaVersion: 1;
+  protocolId: string;
+  policyId: string;
+  circuit: keyof typeof CIRCUIT_PROTOCOL.circuits;
+  protocol: typeof CIRCUIT_PROTOCOL;
+  episodes: (CircuitEpisode & { startWaypoint: number })[];
+  totalTransitions: number;
+  completedLaps: number;
+  successfulEpisodes: number;
+  offTrackEpisodes: number;
+  truncatedEpisodes: number;
+}
+
+/** Uses only the policy's greedy action interface; never calls train/remember. */
+export async function evaluateCircuit(
+  policy: Pick<AgentInterface, 'getAction'>,
+  options: { policyId: string; circuit: keyof typeof CIRCUIT_PROTOCOL.circuits },
+): Promise<CircuitEvaluationReport> {
+  if (!options.policyId.trim()) throw new Error('A versioned policyId is required');
+  const track = CIRCUIT_PROTOCOL.circuits[options.circuit];
+  if (!track) throw new Error('Unknown evaluation circuit');
+  const episodes: CircuitEvaluationReport['episodes'] = [];
+  for (const startWaypoint of CIRCUIT_PROTOCOL.startWaypoints) {
+    const world = new CircuitEnv(track.straightLength, track.radius, track.halfWidth, {
+      startWaypoint, maxSteps: CIRCUIT_PROTOCOL.maxSteps, targetLaps: CIRCUIT_PROTOCOL.targetLaps,
+    });
+    const loop = new IgnitionEnv(world);
+    loop.agent = {
+      async getAction(observation) {
+        const action = await policy.getAction(observation, true);
+        if (typeof action !== 'number' || !Number.isInteger(action) || action < 0 || action > 2) {
+          throw new Error('Circuit policy must return a discrete action in [0, 2]');
+        }
+        return action;
+      },
+      remember() { throw new Error('Evaluation cannot record training experience'); },
+      async train() { throw new Error('Evaluation cannot train'); },
+    };
+    for (let step = 0; step < CIRCUIT_PROTOCOL.maxSteps; step++) {
+      const result = await loop.inferStep();
+      if (result.terminated || result.truncated) break;
+      // Resolved policy promises alone do not yield to rendering or input.
+      if ((step + 1) % 32 === 0) await new Promise<void>(resolve => setTimeout(resolve, 0));
+    }
+    if (!world.lastEpisode) throw new Error('Evaluation did not produce a completed episode');
+    episodes.push({ ...world.lastEpisode, startWaypoint });
+  }
+  return {
+    schemaVersion: 1, protocolId: CIRCUIT_PROTOCOL.id, policyId: options.policyId,
+    circuit: options.circuit, protocol: CIRCUIT_PROTOCOL, episodes,
+    totalTransitions: episodes.reduce((sum, e) => sum + e.transitions, 0),
+    completedLaps: episodes.reduce((sum, e) => sum + e.completedLaps, 0),
+    successfulEpisodes: episodes.filter(e => e.endReason === 'success').length,
+    offTrackEpisodes: episodes.filter(e => e.endReason === 'off-track').length,
+    truncatedEpisodes: episodes.filter(e => e.endReason === 'time-limit').length,
+  };
+}
diff --git a/packages/demo-car-circuit/src/main.tsx b/packages/demo-car-circuit/src/main.tsx
index c5bac04..1dde2ed 100644
--- a/packages/demo-car-circuit/src/main.tsx
+++ b/packages/demo-car-circuit/src/main.tsx
@@ -1,6 +1,6 @@
 import { StrictMode } from 'react';
 import { createRoot } from 'react-dom/client';
-import App from './App';
+import App from './racing/RacingApp';
 import './styles.css';
 
 createRoot(document.getElementById('root')!).render(<StrictMode><App /></StrictMode>);
diff --git a/packages/demo-car-circuit/src/public/drivers/README.md b/packages/demo-car-circuit/src/public/drivers/README.md
new file mode 100644
index 0000000..3aef63a
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/drivers/README.md
@@ -0,0 +1,7 @@
+# Learned demonstration drivers
+
+These are actual network weights trained by `scripts/train-racing.mts`, not rule controllers. Method: imitation with dataset aggregation, 16 fixed rounds, seeds 11/29/47, 65,536 examples per seed, three gradient epochs per round. Same 20-observation/nine-action contract and race physics as the player. Solo collection followed by frozen rule-based traffic.
+
+Checkpoint selection was the final scheduled update on the training track. All three scheduled seeds are included, without held-out selection. Reports include the untrained baseline and every evaluation (three scenario seeds, two circuits, solo/traffic). Aggregate success: 29/36. Each untrained baseline completed zero laps. Seed 11: 9/12, seed 29: 10/12, seed 47: 10/12. Failure reports remain included. Seed 11 fails all three solo Harbour runs; no universal driving competence is claimed. Traffic contains many repeated contact ticks; these drivers are not collision-free racers.
+
+Saved JSON files contain trained weights and contract metadata. Inference reconstructs the network and uses its output directly. No reference controller fallback is present. The reports were measured using TensorFlow.js 4.22 native on macOS arm64; browser backend variation requires a separate browser check.
diff --git a/packages/demo-car-circuit/src/public/drivers/driver-11.json b/packages/demo-car-circuit/src/public/drivers/driver-11.json
new file mode 100644
index 0000000..eb2c7d2
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/drivers/driver-11.json
@@ -0,0 +1 @@
+{"configuration":{"hiddenLayers":[32,32],"learningRate":0.003,"batchSize":128,"epochsPerRound":3,"samplesPerRound":4096},"format":"ignition-driver-v1","algorithm":"imitation-mlp","driving":"circuit-racing-v2","observation":"racing-observation-v1","protocol":"racing-learning-v2","seed":11,"updates":48,"samples":65536,"createdAt":"2026-09-06T17:52:30.333Z","weights":[{"shape":[20,32],"values":[0.7612519264221191,0.15566645562648773,0.6877385973930359,-0.3698507845401764,-0.7449214458465576,0.07730273902416229,0.14995832741260529,-0.5683470964431763,0.7195403575897217,1.0723806619644165,-0.856438934803009,-0.3648903965950012,-0.3670099377632141,-0.46315109729766846,0.525383472442627,-0.6812911629676819,-0.10873377323150635,0.7917916774749756,0.21900597214698792,-0.0011643237667158246,0.5070750713348389,0.34130460023880005,-0.07769997417926788,-1.0206106901168823,-0.6413770914077759,0.5369018912315369,-0.31507542729377747,0.14570794999599457,0.9682663679122925,-0.3907453715801239,0.07775330543518066,-0.13723699748516083,0.5763671398162842,0.16332320868968964,-0.11733322590589523,0.10526241362094879,0.10172218084335327,0.038919538259506226,0.10954318940639496,0.21770627796649933,-0.196794793009758,0.05888370797038078,0.02506248652935028,-0.06277914345264435,-0.013610664755105972,0.1736777275800705,0.42089277505874634,0.16464225947856903,-0.08201368898153305,0.2564795911312103,0.24723954498767853,0.10674552619457245,0.10521550476551056,0.07576068490743637,0.101612389087677,-0.08220699429512024,0.31160828471183777,0.2199295163154602,-0.026912394911050797,0.5364101529121399,-0.38365185260772705,0.20467102527618408,0.04170810431241989,0.14112238585948944,0.3998434841632843,-0.227192685008049,-0.08419710397720337,-0.40718552470207214,-0.1690686047077179,0.6424664258956909,-0.028970271348953247,-0.4525538384914398,-0.16795875132083893,0.3233764171600342,0.35365670919418335,-0.3522176444530487,-0.26252543926239014,0.2985861003398895,-0.5256119966506958,0.10176969319581985,0.6218955516815186,-0.3594473898410797,0.07848546653985977,0.49930906295776367,-0.349727064371109,-0.1975012868642807,0.18074271082878113,-0.31893667578697205,-0.5914115309715271,0.5307361483573914,0.017267121002078056,0.027251172810792923,-0.5233288407325745,-0.05988439917564392,0.27563661336898804,0.22037601470947266,-0.18101780116558075,0.4753718674182892,-0.6758929491043091,-0.477079302072525,0.4516289234161377,-0.3401351869106293,0.3541306257247925,0.451994389295578,0.373321533203125,0.437000572681427,-0.02035553753376007,-0.6407514214515686,0.11089780926704407,0.5898049473762512,0.11506084352731705,-0.19347934424877167,-0.143748939037323,0.4276074171066284,0.5363560318946838,-0.1557435840368271,-0.25342661142349243,0.14268621802330017,0.05244925990700722,-0.19123844802379608,-0.43035921454429626,-0.08003177493810654,0.5179058313369751,0.23618675768375397,0.10306227207183838,-0.33926475048065186,0.22952507436275482,0.28976646065711975,-0.9286502003669739,0.5673623085021973,-1.1781638860702515,-0.7607981562614441,-0.4005643129348755,-2.0657217502593994,1.9600728750228882,1.8476110696792603,0.742678701877594,0.2500031888484955,0.33812788128852844,-1.7298309803009033,1.2821646928787231,1.8244813680648804,-0.24145081639289856,-1.5404815673828125,-1.485015630722046,0.7748294472694397,1.5077459812164307,1.3481512069702148,-1.5019097328186035,1.5209523439407349,-2.0569369792938232,0.5739914178848267,-0.04291415959596634,1.180751085281372,0.81947922706604,-1.5517576932907104,0.0876818597316742,1.9478868246078491,-1.944172739982605,-1.132859468460083,-0.18799461424350739,-0.11239457875490189,0.15369310975074768,0.3061271905899048,-0.044452134519815445,-0.05338265746831894,-0.2880171537399292,0.5558185577392578,0.26100650429725647,-0.013323670253157616,0.21222522854804993,-0.22506549954414368,0.05743710696697235,-0.24022549390792847,0.25543731451034546,-0.08189259469509125,0.0994541347026825,0.2650046646595001,0.37753772735595703,-0.23703747987747192,-0.10478576272726059,-0.1899448037147522,-0.3076202869415283,0.43360891938209534,0.0011114008957520127,0.21801772713661194,-0.45686987042427063,-0.02853872999548912,0.17272916436195374,-0.12786339223384857,-0.3924940228462219,0.010622626170516014,0.15972506999969482,0.400329053401947,0.0537518747150898,0.47106364369392395,-0.16368891298770905,-0.051336079835891724,-0.3660120666027069,0.24083596467971802,-0.04005957394838333,-0.17360195517539978,0.014155827462673187,-0.20383566617965698,0.18165893852710724,-0.12950211763381958,-0.1931816041469574,0.10269830375909805,-0.08379243314266205,-0.08467528969049454,0.10028453171253204,0.22210998833179474,0.2123079001903534,0.11168527603149414,0.05596819147467613,-0.18802036345005035,0.12623363733291626,-0.14842376112937927,-0.06016337499022484,0.306450754404068,0.12078942358493805,0.2839212417602539,-0.08275843411684036,-0.2575736343860626,-0.009809627197682858,-0.19917240738868713,-0.0871986672282219,-0.22774477303028107,0.047099556773900986,0.04203188791871071,0.083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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/drivers/driver-29.json b/packages/demo-car-circuit/src/public/drivers/driver-29.json
new file mode 100644
index 0000000..b2f5ca1
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/drivers/driver-29.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/drivers/driver-47.json b/packages/demo-car-circuit/src/public/drivers/driver-47.json
new file mode 100644
index 0000000..9224739
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/drivers/driver-47.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/drivers/protocol.json b/packages/demo-car-circuit/src/public/drivers/protocol.json
new file mode 100644
index 0000000..2cd508e
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/drivers/protocol.json
@@ -0,0 +1,48 @@
+{
+  "protocol": {
+    "id": "racing-learning-v2",
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+    "observation": "racing-observation-v1",
+    "race": "circuit-race-v2",
+    "trainingSeeds": [
+      11,
+      29,
+      47
+    ],
+    "checkpointSelection": "last scheduled update on training circuit only",
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+    },
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+  },
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+    11,
+    29,
+    47
+  ],
+  "rounds": 16,
+  "sourceHashes": {
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+    "packages/demo-car-circuit/src/racing/learning-protocol.ts": "577e69cbc007db07cde13feeb5152a8a6f0e7bb2e530db580bdb25fe1e1e128a",
+    "packages/demo-car-circuit/src/racing/learned-driver.ts": "67dd9890d773d8f1935f6b02da8585da9e09bbb6190af0239e37c71bc548e361"
+  },
+  "head": "5847b7651daebef749d438c7e45feb32c3cbbd28",
+  "createdAt": "2026-09-06T17:52:16.391Z",
+  "node": "v22.14.0",
+  "tfjs": "4.22.0",
+  "backend": "tensorflow",
+  "platform": "darwin",
+  "arch": "arm64"
+}
\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/drivers/report-11.json b/packages/demo-car-circuit/src/public/drivers/report-11.json
new file mode 100644
index 0000000..58fcb55
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/drivers/report-11.json
@@ -0,0 +1,186 @@
+{
+  "protocol": {
+    "id": "racing-learning-v2",
+    "driving": "circuit-racing-v2",
+    "observation": "racing-observation-v1",
+    "race": "circuit-race-v2",
+    "trainingSeeds": [
+      11,
+      29,
+      47
+    ],
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+    "evaluationSeeds": [
+      101,
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+    },
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+    "rescues": 60,
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diff --git a/packages/demo-car-circuit/src/public/drivers/report-29.json b/packages/demo-car-circuit/src/public/drivers/report-29.json
new file mode 100644
index 0000000..5910da8
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/drivers/report-29.json
@@ -0,0 +1,186 @@
+{
+  "protocol": {
+    "id": "racing-learning-v2",
+    "driving": "circuit-racing-v2",
+    "observation": "racing-observation-v1",
+    "race": "circuit-race-v2",
+    "trainingSeeds": [
+      11,
+      29,
+      47
+    ],
+    "checkpointSelection": "last scheduled update on training circuit only",
+    "evaluationSeeds": [
+      101,
+      307,
+      509
+    ],
+    "success": {
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+      "maxRescues": 0,
+      "maxPenalties": 10,
+      "minimumCompletionRate": 0.8
+    },
+    "evaluation": "All three training seeds on both circuits, solo and three frozen rule-based traffic drivers. Report every run, including failures."
+  },
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/drivers/report-47.json b/packages/demo-car-circuit/src/public/drivers/report-47.json
new file mode 100644
index 0000000..797d290
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/drivers/report-47.json
@@ -0,0 +1,186 @@
+{
+  "protocol": {
+    "id": "racing-learning-v2",
+    "driving": "circuit-racing-v2",
+    "observation": "racing-observation-v1",
+    "race": "circuit-race-v2",
+    "trainingSeeds": [
+      11,
+      29,
+      47
+    ],
+    "checkpointSelection": "last scheduled update on training circuit only",
+    "evaluationSeeds": [
+      101,
+      307,
+      509
+    ],
+    "success": {
+      "completedLaps": 3,
+      "maxRescues": 0,
+      "maxPenalties": 10,
+      "minimumCompletionRate": 0.8
+    },
+    "evaluation": "All three training seeds on both circuits, solo and three frozen rule-based traffic drivers. Report every run, including failures."
+  },
+  "rounds": 16,
+  "untrained": {
+    "seed": 101,
+    "test": false,
+    "traffic": false,
+    "completed": false,
+    "success": false,
+    "laps": 0,
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+    "penaltySeconds": 282,
+    "rescues": 56,
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/models/License.txt b/packages/demo-car-circuit/src/public/models/License.txt
new file mode 100644
index 0000000..60bca66
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/models/License.txt
@@ -0,0 +1,28 @@
+	
+
+	Car Kit (3.1)
+
+	Created/distributed by Kenney (www.kenney.nl)
+	Creation date: 02-04-2026 14:03
+	
+			------------------------------
+
+	License: (Creative Commons Zero, CC0)
+	http://creativecommons.org/publicdomain/zero/1.0/
+
+	You can use this content for personal, educational, and commercial purposes.
+
+	Support by crediting 'Kenney' or 'www.kenney.nl' (this is not a requirement)
+
+			------------------------------
+
+	• Website : www.kenney.nl
+	• Donate  : www.kenney.nl/donate
+
+	• Patreon : patreon.com/kenney
+	
+	Follow on social media for updates:
+
+	• Twitter:	 twitter.com/KenneyNL
+	• BlueSky:	 kenney.bsky.social
+	• Instagram: instagram.com/kenney_nl
\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/models/PROVENANCE.md b/packages/demo-car-circuit/src/public/models/PROVENANCE.md
new file mode 100644
index 0000000..f5b337f
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/models/PROVENANCE.md
@@ -0,0 +1,10 @@
+# Kenney Car Kit 3.1
+
+Source: https://kenney.nl/assets/car-kit
+
+Archive: https://kenney.nl/media/pages/assets/car-kit/1a312ec241-1775131960/kenney_car-kit.zip
+
+License: CC0; original License.txt included. GLB files copied without modification.
+
+- race.glb: SHA256 e1408c4c6e135f5a39c5f34cbfbcac9ae21c5c491e793fdee74bccac8334d942
+- sedan-sports.glb: SHA256 2889c428ca1dd9c975c3ff760eb8757883490410555ab60b818f712f583826d6
\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/models/Textures/colormap.png b/packages/demo-car-circuit/src/public/models/Textures/colormap.png
new file mode 100644
index 0000000..5c4b185
Binary files /dev/null and b/packages/demo-car-circuit/src/public/models/Textures/colormap.png differ
diff --git a/packages/demo-car-circuit/src/public/models/race.glb b/packages/demo-car-circuit/src/public/models/race.glb
new file mode 100644
index 0000000..69d4315
Binary files /dev/null and b/packages/demo-car-circuit/src/public/models/race.glb differ
diff --git a/packages/demo-car-circuit/src/public/models/sedan-sports.glb b/packages/demo-car-circuit/src/public/models/sedan-sports.glb
new file mode 100644
index 0000000..07442cb
Binary files /dev/null and b/packages/demo-car-circuit/src/public/models/sedan-sports.glb differ
diff --git a/packages/demo-car-circuit/src/public/reports/imitation-v1/README.md b/packages/demo-car-circuit/src/public/reports/imitation-v1/README.md
new file mode 100644
index 0000000..c8683d9
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/imitation-v1/README.md
@@ -0,0 +1 @@
+Historical imitation V1 checkpoints and reports. Race V1 lacked boundary collisions and rescued any five-second off-road excursion. These weights are incompatible with race/driving V2; no current acceptance claim.
diff --git a/packages/demo-car-circuit/src/public/reports/imitation-v1/driver-11.json b/packages/demo-car-circuit/src/public/reports/imitation-v1/driver-11.json
new file mode 100644
index 0000000..3843347
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/imitation-v1/driver-11.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/imitation-v1/driver-29.json b/packages/demo-car-circuit/src/public/reports/imitation-v1/driver-29.json
new file mode 100644
index 0000000..0d98815
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/imitation-v1/driver-29.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/imitation-v1/driver-47.json b/packages/demo-car-circuit/src/public/reports/imitation-v1/driver-47.json
new file mode 100644
index 0000000..f657465
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/imitation-v1/driver-47.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/imitation-v1/report-11.json b/packages/demo-car-circuit/src/public/reports/imitation-v1/report-11.json
new file mode 100644
index 0000000..c2c36f5
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/imitation-v1/report-11.json
@@ -0,0 +1,186 @@
+{
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diff --git a/packages/demo-car-circuit/src/public/reports/imitation-v1/report-29.json b/packages/demo-car-circuit/src/public/reports/imitation-v1/report-29.json
new file mode 100644
index 0000000..100992b
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/imitation-v1/report-29.json
@@ -0,0 +1,186 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/imitation-v1/report-47.json b/packages/demo-car-circuit/src/public/reports/imitation-v1/report-47.json
new file mode 100644
index 0000000..a90ce7a
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/imitation-v1/report-47.json
@@ -0,0 +1,186 @@
+{
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-101.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-101.json
new file mode 100644
index 0000000..7b23d28
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-101.json
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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-11.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-11.json
new file mode 100644
index 0000000..a6a1820
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-11.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-29.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-29.json
new file mode 100644
index 0000000..22abba2
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-29.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-47.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-47.json
new file mode 100644
index 0000000..ebf0cd5
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-47.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-73.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-73.json
new file mode 100644
index 0000000..4686c19
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/double-dqn-73.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v1","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"episodeTransitions":600,"trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-101.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-101.json
new file mode 100644
index 0000000..a489730
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-101.json
@@ -0,0 +1 @@
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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-11.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-11.json
new file mode 100644
index 0000000..17dcf15
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-11.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-29.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-29.json
new file mode 100644
index 0000000..d6700cf
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-29.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-47.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-47.json
new file mode 100644
index 0000000..35b33e7
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-47.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-73.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-73.json
new file mode 100644
index 0000000..8fa0a7e
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v1/dqn-73.json
@@ -0,0 +1 @@
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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/index.html b/packages/demo-car-circuit/src/public/reports/racing-q-v1/index.html
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@@ -0,0 +1,9 @@
+<!doctype html><html lang="fr"><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>DQN / Double DQN — Circuit Racing</title><style>
+*{box-sizing:border-box}body{margin:0;background:#101517;color:#e8ecea;font:16px/1.65 system-ui,sans-serif}main{max-width:1080px;margin:auto;padding:48px 24px}a{color:#e6ee58}h1{font-size:clamp(32px,5vw,54px);line-height:1.1;letter-spacing:-.04em}h2{margin-top:44px;font-size:24px}.eyebrow{color:#e6ee58;font-size:12px;letter-spacing:.16em}.muted{color:#a7b4b0}.cards{display:grid;grid-template-columns:repeat(auto-fit,minmax(230px,1fr));gap:20px}.card{padding:24px;border:1px solid #37403d}.score{font-size:44px;color:#e6ee58;font-weight:700}.scroll{overflow-x:auto}table{border-collapse:collapse;width:100%;font-variant-numeric:tabular-nums}td,th{text-align:left;padding:14px 12px;border-bottom:1px solid #37403d;white-space:nowrap}th{color:#a7b4b0;font-size:13px}code{overflow-wrap:anywhere}.note{padding:20px;border-left:3px solid #e6ee58;background:#1b2222}li{margin-bottom:8px}</style>
+<main><p class="note">ARCHIVE V1 : cet entraînement arrêtait les sorties de piste. Il ne satisfait pas le contrat de récupération approuvé. Consulter le <a href="../racing-q-v2/index.html">protocole corrigé V2</a> pour la validation finale.</p><a href="../../">← Circuit Racing</a><p class="eyebrow">EXPÉRIENCE REPRODUCTIBLE · RACING Q-LEARNING V1</p><h1>DQN et Double DQN,<br>à budget égal.</h1><p class="muted">Cinq graines par méthode · 20 courses figées par pilote · 20 000 transitions d’entraînement · tensorflow, TensorFlow.js 4.22.0</p>
+<div class="cards"><section class="card"><h2 style="margin:0">DQN</h2><div class="score">68/100</div><p>courses réussies · 68/100 terminées</p><p class="muted">Réussite par graine : moyenne 68.0 %, écart-type 35.9 points ; 0.0–100.0 %.</p></section><section class="card"><h2 style="margin:0">Double DQN</h2><div class="score">89/100</div><p>courses réussies · 89/100 terminées</p><p class="muted">Réussite par graine : moyenne 89.0 %, écart-type 11.6 points ; 75.0–100.0 %.</p></section></div>
+<p class="note">Ces résultats décrivent ce circuit, ce curriculum et ce budget. Ils ne démontrent pas une supériorité générale de l’un des algorithmes. Les graines d’évaluation ne varient que légèrement le cap initial ; ces courses ne constituent pas 200 scénarios indépendants. Tous les checkpoints finaux et tous les échecs sont conservés.</p>
+<h2>Chaque graine, sans sélection</h2><div class="scroll"><table><thead><tr><th>Méthode</th><th>Graine</th><th>Réussies</th><th>Terminées</th><th>Entraînement</th><th>Évaluation</th><th>Checkpoint</th></tr></thead><tbody><tr><td>dqn</td><td>11</td><td>0/20</td><td>0/20</td><td>13.0 s</td><td>24.5 s</td><td><a href="dqn-11.json">Poids</a></td></tr><tr><td>dqn</td><td>29</td><td>15/20</td><td>15/20</td><td>9.9 s</td><td>19.0 s</td><td><a href="dqn-29.json">Poids</a></td></tr><tr><td>dqn</td><td>47</td><td>14/20</td><td>14/20</td><td>10.4 s</td><td>16.6 s</td><td><a href="dqn-47.json">Poids</a></td></tr><tr><td>dqn</td><td>73</td><td>20/20</td><td>20/20</td><td>11.0 s</td><td>19.2 s</td><td><a href="dqn-73.json">Poids</a></td></tr><tr><td>dqn</td><td>101</td><td>19/20</td><td>19/20</td><td>11.5 s</td><td>19.1 s</td><td><a href="dqn-101.json">Poids</a></td></tr><tr><td>double-dqn</td><td>11</td><td>15/20</td><td>15/20</td><td>12.2 s</td><td>21.0 s</td><td><a href="double-dqn-11.json">Poids</a></td></tr><tr><td>double-dqn</td><td>29</td><td>20/20</td><td>20/20</td><td>11.4 s</td><td>15.0 s</td><td><a href="double-dqn-29.json">Poids</a></td></tr><tr><td>double-dqn</td><td>47</td><td>19/20</td><td>19/20</td><td>15.0 s</td><td>10.5 s</td><td><a href="double-dqn-47.json">Poids</a></td></tr><tr><td>double-dqn</td><td>73</td><td>15/20</td><td>15/20</td><td>11.4 s</td><td>19.7 s</td><td><a href="double-dqn-73.json">Poids</a></td></tr><tr><td>double-dqn</td><td>101</td><td>20/20</td><td>20/20</td><td>11.8 s</td><td>14.4 s</td><td><a href="double-dqn-101.json">Poids</a></td></tr></tbody></table></div>
+<h2>Ce qui est identique</h2><ul><li>Réseau 20 → 32 → 32 → 9, ReLU et sorties Q linéaires ; Adam 0,001 ; replay 10 000 ; batch 32 ; gamma 0,99.</li><li>20 000 décisions, chacune maintenue six ticks physiques de 1/60 s. Mise à jour toutes les quatre transitions et copie cible toutes les 100 mises à jour. Dernier checkpoint programmé ; aucune sélection sur l’évaluation.</li><li>Exploration, replay et initialisation pilotés par la graine. Pas de mélange aléatoire supplémentaire dans fit.</li><li>Entraînement solo sur Alpine Park : départs variés sur la piste, vitesse initiale 4–12 m/s, épisodes limités à 600 décisions et arrêt anticipé lors d’une sortie. Récompense de progression, checkpoints et pénalités. Aucun label ni remplacement par un pilote à règles.</li><li>Évaluation complète : cinq caps initiaux par circuit et par condition solo/trafic. Alpine Park et Harbour Test, trois tours, plafond 300 s. Trafic constitué de références à règles figées identiques pour les deux méthodes.</li><li>Réussite : trois tours, aucune remise en piste, pénalité ≤ 10 s. Une course terminée peut échouer à ce critère.</li></ul>
+<h2>Temps et limites</h2><p>Temps réels mesurés sur darwin/arm64, Node v22.14.0. Le premier passage inclut davantage de démarrage à froid ; les temps ne servent pas de classement de vitesse. Le curriculum d’entraînement diffère des départs de course arrêtés et ne comprend pas de trafic : les échecs de transfert restent mesurés.</p><p>Les reprises navigateur rechargent les poids et recréent optimiseur, replay et exploration. Ce benchmark utilise uniquement des entraînements neufs. Les poids de cette page sont des résultats expérimentaux ; leur présence ne garantit pas un adversaire compétent.</p>
+<p><a href="results.json" download>Télécharger les 200 résultats bruts, paramètres, versions et empreintes des sources</a></p><p class="muted">Reproduction : <code>pnpm exec tsx --tsconfig scripts/tsconfig.racing.json scripts/compare-racing-q.mts</code>, puis <code>node scripts/report-racing-q.mjs</code>. Le script refuse d’écraser un protocole existant ; déplacer le dossier de résultats avant une nouvelle expérience.</p><p>Calcul Double DQN : sélection online, évaluation cible, selon <a href="https://arxiv.org/abs/1509.06461">van Hasselt, Guez et Silver</a>.</p></main></html>
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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v1/results.json b/packages/demo-car-circuit/src/public/reports/racing-q-v1/results.json
new file mode 100644
index 0000000..8725584
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+}
\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-101.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-101.json
new file mode 100644
index 0000000..fceaf1e
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-101.json
@@ -0,0 +1 @@
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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-11.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-11.json
new file mode 100644
index 0000000..6ed5496
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-11.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v2","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-29.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-29.json
new file mode 100644
index 0000000..1939b12
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-29.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v2","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-47.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-47.json
new file mode 100644
index 0000000..317d111
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-47.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v2","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-73.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-73.json
new file mode 100644
index 0000000..eb9d1c1
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/double-dqn-73.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v2","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-101.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-101.json
new file mode 100644
index 0000000..9698e64
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-101.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v2","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-11.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-11.json
new file mode 100644
index 0000000..e0060b3
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-11.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v2","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-29.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-29.json
new file mode 100644
index 0000000..0fb3b2f
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-29.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v2","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-47.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-47.json
new file mode 100644
index 0000000..50fdb81
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-47.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v2","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-73.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-73.json
new file mode 100644
index 0000000..370bd11
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v2/dqn-73.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v2","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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+<!doctype html><html lang="fr"><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>DQN / Double DQN — Circuit Racing</title><style>
+*{box-sizing:border-box}body{margin:0;background:#101517;color:#e8ecea;font:16px/1.65 system-ui,sans-serif}main{max-width:1080px;margin:auto;padding:48px 24px}a{color:#e6ee58}h1{font-size:clamp(32px,5vw,54px);line-height:1.1;letter-spacing:-.04em}h2{margin-top:44px;font-size:24px}.eyebrow{color:#e6ee58;font-size:12px;letter-spacing:.16em}.muted{color:#a7b4b0}.cards{display:grid;grid-template-columns:repeat(auto-fit,minmax(230px,1fr));gap:20px}.card{padding:24px;border:1px solid #37403d}.score{font-size:44px;color:#e6ee58;font-weight:700}.scroll{overflow-x:auto}table{border-collapse:collapse;width:100%;font-variant-numeric:tabular-nums}td,th{text-align:left;padding:14px 12px;border-bottom:1px solid #37403d;white-space:nowrap}th{color:#a7b4b0;font-size:13px}code{overflow-wrap:anywhere}.note{padding:20px;border-left:3px solid #e6ee58;background:#1b2222}li{margin-bottom:8px}</style>
+<main><p class="note"><strong>Archive — règles incomplètes.</strong> Cette V2 utilisait la course V1 : aucune collision avec les limites et remise après cinq secondes hors piste, même en mouvement. Les résultats ne valident pas les règles corrigées. <a href="../racing-q-v3/index.html">Consulter la campagne V3 avec les règles corrigées.</a></p><a href="../../">← Circuit Racing</a><p class="eyebrow">EXPÉRIENCE REPRODUCTIBLE · RACING Q-LEARNING V2</p><h1>DQN et Double DQN,<br>à budget égal.</h1><p class="muted">Cinq graines par méthode · 20 courses figées par pilote · 20 000 transitions d’entraînement · tensorflow, TensorFlow.js 4.22.0</p>
+<div class="cards"><section class="card"><h2 style="margin:0">DQN</h2><div class="score">37/100</div><p>courses réussies · 49/100 terminées</p><p class="muted">Réussite par graine : moyenne 37.0 %, écart-type 28.0 points ; 0.0–85.0 %.</p></section><section class="card"><h2 style="margin:0">Double DQN</h2><div class="score">37/100</div><p>courses réussies · 56/100 terminées</p><p class="muted">Réussite par graine : moyenne 37.0 %, écart-type 11.2 points ; 15.0–45.0 %.</p></section></div>
+<p class="note">Ces résultats décrivent ce circuit, ce curriculum et ce budget. Ils ne démontrent pas une supériorité générale de l’un des algorithmes. Les graines d’évaluation ne varient que légèrement le cap initial ; ces courses ne constituent pas 200 scénarios indépendants. Tous les checkpoints finaux et tous les échecs sont conservés.</p>
+<h2>Chaque graine, sans sélection</h2><div class="scroll"><table><thead><tr><th>Méthode</th><th>Graine</th><th>Réussies</th><th>Terminées</th><th>Entraînement</th><th>Évaluation</th><th>Checkpoint</th></tr></thead><tbody><tr><td>dqn</td><td>11</td><td>17/20</td><td>17/20</td><td>15.4 s</td><td>16.6 s</td><td><a href="dqn-11.json">Poids</a></td></tr><tr><td>dqn</td><td>29</td><td>9/20</td><td>9/20</td><td>15.3 s</td><td>24.6 s</td><td><a href="dqn-29.json">Poids</a></td></tr><tr><td>dqn</td><td>47</td><td>0/20</td><td>1/20</td><td>15.1 s</td><td>24.1 s</td><td><a href="dqn-47.json">Poids</a></td></tr><tr><td>dqn</td><td>73</td><td>5/20</td><td>12/20</td><td>14.4 s</td><td>19.8 s</td><td><a href="dqn-73.json">Poids</a></td></tr><tr><td>dqn</td><td>101</td><td>6/20</td><td>10/20</td><td>21.7 s</td><td>20.9 s</td><td><a href="dqn-101.json">Poids</a></td></tr><tr><td>double-dqn</td><td>11</td><td>8/20</td><td>14/20</td><td>16.0 s</td><td>20.1 s</td><td><a href="double-dqn-11.json">Poids</a></td></tr><tr><td>double-dqn</td><td>29</td><td>8/20</td><td>12/20</td><td>15.4 s</td><td>19.5 s</td><td><a href="double-dqn-29.json">Poids</a></td></tr><tr><td>double-dqn</td><td>47</td><td>3/20</td><td>5/20</td><td>15.0 s</td><td>19.7 s</td><td><a href="double-dqn-47.json">Poids</a></td></tr><tr><td>double-dqn</td><td>73</td><td>9/20</td><td>11/20</td><td>15.2 s</td><td>17.0 s</td><td><a href="double-dqn-73.json">Poids</a></td></tr><tr><td>double-dqn</td><td>101</td><td>9/20</td><td>14/20</td><td>15.5 s</td><td>20.8 s</td><td><a href="double-dqn-101.json">Poids</a></td></tr></tbody></table></div>
+<h2>Ce qui est identique</h2><ul><li>Réseau 20 → 32 → 32 → 9, ReLU et sorties Q linéaires ; Adam 0,001 ; replay 10 000 ; batch 32 ; gamma 0,99.</li><li>20 000 décisions, chacune maintenue six ticks physiques de 1/60 s. Mise à jour toutes les quatre transitions et copie cible toutes les 100 mises à jour. Dernier checkpoint programmé ; aucune sélection sur l’évaluation.</li><li>Exploration, replay et initialisation pilotés par la graine. Pas de mélange aléatoire supplémentaire dans fit.</li><li>Entraînement sur Alpine Park : 10 000 décisions solo, puis 10 000 avec trois références figées ; départs variés sur la piste, vitesse initiale 4–12 m/s, épisodes limités à 600 décisions. Les sorties restent récupérables avec les mêmes remises en piste et pénalités que la course. Récompense de progression, checkpoints et pénalités. Aucun label ni remplacement par un pilote à règles.</li><li>Évaluation complète : cinq caps initiaux par circuit et par condition solo/trafic. Alpine Park et Harbour Test, trois tours, plafond 300 s. Trafic constitué de références à règles figées identiques pour les deux méthodes.</li><li>Réussite : trois tours, aucune remise en piste, pénalité ≤ 10 s. Une course terminée peut échouer à ce critère.</li></ul>
+<h2>Temps et limites</h2><p>Temps réels mesurés sur darwin/arm64, Node v22.14.0. Le premier passage inclut davantage de démarrage à froid ; les temps ne servent pas de classement de vitesse. Les départs variés du curriculum d’entraînement diffèrent des départs de course arrêtés : les échecs de transfert restent mesurés.</p><p>Les reprises navigateur rechargent les poids et recréent optimiseur, replay et exploration. Ce benchmark utilise uniquement des entraînements neufs. Les poids de cette page sont des résultats expérimentaux ; leur présence ne garantit pas un adversaire compétent.</p>
+<p><a href="results.json" download>Télécharger les 200 résultats bruts, paramètres, versions et empreintes des sources</a></p><p class="muted">Reproduction : <code>pnpm exec tsx --tsconfig scripts/tsconfig.racing.json scripts/compare-racing-q.mts</code>, puis <code>node scripts/report-racing-q.mjs</code>. Le script refuse d’écraser un protocole existant ; déplacer le dossier de résultats avant une nouvelle expérience.</p><p>Calcul Double DQN : sélection online, évaluation cible, selon <a href="https://arxiv.org/abs/1509.06461">van Hasselt, Guez et Silver</a>.</p></main></html>
\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v2/results.json b/packages/demo-car-circuit/src/public/reports/racing-q-v2/results.json
new file mode 100644
index 0000000..3fb0c71
--- /dev/null
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+}
\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-101.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-101.json
new file mode 100644
index 0000000..a436c0b
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-101.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-11.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-11.json
new file mode 100644
index 0000000..d7e2c08
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-11.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v3","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-29.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-29.json
new file mode 100644
index 0000000..7b3d6b5
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-29.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v3","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-47.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-47.json
new file mode 100644
index 0000000..9b99eb2
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-47.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-73.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-73.json
new file mode 100644
index 0000000..507153d
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/double-dqn-73.json
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-101.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-101.json
new file mode 100644
index 0000000..d9f4bcd
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-101.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v3","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-11.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-11.json
new file mode 100644
index 0000000..dafc1d1
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-11.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v3","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-29.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-29.json
new file mode 100644
index 0000000..2ff787f
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-29.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v3","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-47.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-47.json
new file mode 100644
index 0000000..8443e4f
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-47.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v3","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-73.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-73.json
new file mode 100644
index 0000000..eac45f8
--- /dev/null
+++ b/packages/demo-car-circuit/src/public/reports/racing-q-v3/dqn-73.json
@@ -0,0 +1 @@
+{"configuration":{"id":"racing-q-learning-v3","seeds":[11,29,47,73,101],"evaluationSeeds":[211,307,419,509,601],"transitions":20000,"actionRepeat":6,"trafficAfterTransitions":10000,"episodeTransitions":600,"termination":"finish only; off-road recovery follows RaceWorld","truncation":"600 decisions, race time limit, or solo-to-traffic curriculum boundary","trainEvery":4,"hiddenLayers":[32,32],"learningRate":0.001,"batchSize":32,"memorySize":10000,"gamma":0.99,"epsilonDecay":0.9995,"minEpsilon":0.05,"targetUpdateFrequency":100,"evaluation":"5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy","selection":"last scheduled checkpoint; all failures 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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/index.html b/packages/demo-car-circuit/src/public/reports/racing-q-v3/index.html
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+<!doctype html><html lang="fr"><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>DQN / Double DQN — Circuit Racing</title><style>
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+<main><a href="../../">← Circuit Racing</a><p class="eyebrow">EXPÉRIENCE REPRODUCTIBLE · RACING Q-LEARNING V3</p><h1>DQN et Double DQN,<br>à budget égal.</h1><p class="muted">Cinq graines par méthode · 20 courses figées par pilote · 20 000 transitions d’entraînement · tensorflow, TensorFlow.js 4.22.0</p>
+<div class="cards"><section class="card"><h2 style="margin:0">DQN</h2><div class="score">42/100</div><p>courses réussies · 61/100 terminées</p><p class="muted">Réussite par graine : moyenne 42.0 %, écart-type 38.0 points ; 0.0–85.0 %.</p></section><section class="card"><h2 style="margin:0">Double DQN</h2><div class="score">33/100</div><p>courses réussies · 77/100 terminées</p><p class="muted">Réussite par graine : moyenne 33.0 %, écart-type 33.0 points ; 0.0–80.0 %.</p></section></div>
+<p class="note">Ces résultats décrivent ce circuit, ce curriculum et ce budget. Ils ne démontrent pas une supériorité générale de l’un des algorithmes. Les graines d’évaluation ne varient que légèrement le cap initial ; ces courses ne constituent pas 200 scénarios indépendants. Tous les checkpoints finaux et tous les échecs sont conservés.</p>
+<h2>Chaque graine, sans sélection</h2><div class="scroll"><table><thead><tr><th>Méthode</th><th>Graine</th><th>Réussies</th><th>Terminées</th><th>Entraînement</th><th>Évaluation</th><th>Checkpoint</th></tr></thead><tbody><tr><td>dqn</td><td>11</td><td>17/20</td><td>20/20</td><td>20.3 s</td><td>18.3 s</td><td><a href="dqn-11.json">Poids</a></td></tr><tr><td>dqn</td><td>29</td><td>0/20</td><td>6/20</td><td>19.4 s</td><td>27.7 s</td><td><a href="dqn-29.json">Poids</a></td></tr><tr><td>dqn</td><td>47</td><td>0/20</td><td>2/20</td><td>18.9 s</td><td>33.0 s</td><td><a href="dqn-47.json">Poids</a></td></tr><tr><td>dqn</td><td>73</td><td>8/20</td><td>14/20</td><td>23.4 s</td><td>24.2 s</td><td><a href="dqn-73.json">Poids</a></td></tr><tr><td>dqn</td><td>101</td><td>17/20</td><td>19/20</td><td>18.5 s</td><td>19.9 s</td><td><a href="dqn-101.json">Poids</a></td></tr><tr><td>double-dqn</td><td>11</td><td>13/20</td><td>20/20</td><td>19.4 s</td><td>14.8 s</td><td><a href="double-dqn-11.json">Poids</a></td></tr><tr><td>double-dqn</td><td>29</td><td>16/20</td><td>20/20</td><td>19.6 s</td><td>17.4 s</td><td><a href="double-dqn-29.json">Poids</a></td></tr><tr><td>double-dqn</td><td>47</td><td>3/20</td><td>11/20</td><td>19.6 s</td><td>23.2 s</td><td><a href="double-dqn-47.json">Poids</a></td></tr><tr><td>double-dqn</td><td>73</td><td>1/20</td><td>10/20</td><td>19.7 s</td><td>26.1 s</td><td><a href="double-dqn-73.json">Poids</a></td></tr><tr><td>double-dqn</td><td>101</td><td>0/20</td><td>16/20</td><td>18.9 s</td><td>24.6 s</td><td><a href="double-dqn-101.json">Poids</a></td></tr></tbody></table></div>
+<h2>Ce qui est identique</h2><ul><li>Réseau 20 → 32 → 32 → 9, ReLU et sorties Q linéaires ; Adam 0,001 ; replay 10 000 ; batch 32 ; gamma 0,99.</li><li>20 000 décisions, chacune maintenue six ticks physiques de 1/60 s. Mise à jour toutes les quatre transitions et copie cible toutes les 100 mises à jour. Dernier checkpoint programmé ; aucune sélection sur l’évaluation.</li><li>Exploration, replay et initialisation pilotés par la graine. Pas de mélange aléatoire supplémentaire dans fit.</li><li>Entraînement sur Alpine Park : 10 000 décisions solo, puis 10 000 avec trois références figées ; départs variés sur la piste, vitesse initiale 4–12 m/s, épisodes limités à 600 décisions. Les sorties restent récupérables avec les mêmes remises en piste et pénalités que la course. Récompense de progression, checkpoints et pénalités. Aucun label ni remplacement par un pilote à règles.</li><li>Conduite et course V2 : barrières continues, collisions voiture/voiture et voiture/rail, sortie ralentie (+2 s), remise après immobilisation dans un rayon de 1 m pendant 5 s (+5 s). Les mêmes contraintes valent dans tous les modes.</li><li>Évaluation complète : cinq caps initiaux par circuit et par condition solo/trafic. Alpine Park et Harbour Test, trois tours, plafond 300 s. Trafic constitué de références à règles figées identiques pour les deux méthodes.</li><li>Réussite : trois tours, aucune remise en piste, pénalité ≤ 10 s. Une course terminée peut échouer à ce critère.</li></ul>
+<h2>Temps et limites</h2><p>Temps réels mesurés sur darwin/arm64, Node v22.14.0, Apple M4, 16 Gio. La machine exécutait aussi la démo navigateur ; les temps ne proviennent pas d’une machine isolée. Le premier passage inclut davantage de démarrage à froid ; les temps ne servent pas de classement de vitesse. Les départs variés du curriculum d’entraînement diffèrent des départs de course arrêtés : les échecs de transfert restent mesurés.</p><p>Les reprises navigateur rechargent les poids et recréent optimiseur, replay et exploration. Ce benchmark utilise uniquement des entraînements neufs. Les poids de cette page sont des résultats expérimentaux ; leur présence ne garantit pas un adversaire compétent.</p>
+<p><a href="results.json" download>Télécharger les 200 résultats bruts, paramètres, versions et empreintes des sources</a></p><p class="muted">Reproduction : <code>pnpm exec tsx --tsconfig scripts/tsconfig.racing.json scripts/compare-racing-q.mts</code>, puis <code>node scripts/report-racing-q.mjs</code>. Le script refuse d’écraser un protocole existant ; déplacer le dossier de résultats avant une nouvelle expérience.</p><p>Calcul Double DQN : sélection online, évaluation cible, selon <a href="https://arxiv.org/abs/1509.06461">van Hasselt, Guez et Silver</a>.</p></main></html>
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diff --git a/packages/demo-car-circuit/src/public/reports/racing-q-v3/results.json b/packages/demo-car-circuit/src/public/reports/racing-q-v3/results.json
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+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 201.76666666666665,
+          "penaltySeconds": 22,
+          "rescues": 0,
+          "contacts": 3800
+        },
+        {
+          "seed": 307,
+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 224.01666666666665,
+          "penaltySeconds": 31,
+          "rescues": 1,
+          "contacts": 3659
+        },
+        {
+          "seed": 419,
+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 174.25,
+          "penaltySeconds": 26,
+          "rescues": 0,
+          "contacts": 3476
+        },
+        {
+          "seed": 509,
+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 177.55,
+          "penaltySeconds": 20,
+          "rescues": 0,
+          "contacts": 4543
+        },
+        {
+          "seed": 601,
+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 221.66666666666666,
+          "penaltySeconds": 30,
+          "rescues": 0,
+          "contacts": 4527
+        }
+      ]
+    },
+    {
+      "algorithm": "double-dqn",
+      "seed": 101,
+      "trainingSeconds": 18.915386750000007,
+      "evaluationSeconds": 24.592432750000036,
+      "transitions": 20000,
+      "updates": 4993,
+      "backend": "tensorflow",
+      "reports": [
+        {
+          "seed": 211,
+          "test": false,
+          "traffic": false,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 183.28333333333333,
+          "penaltySeconds": 52,
+          "rescues": 0,
+          "contacts": 99
+        },
+        {
+          "seed": 307,
+          "test": false,
+          "traffic": false,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 124.5,
+          "penaltySeconds": 44,
+          "rescues": 0,
+          "contacts": 20
+        },
+        {
+          "seed": 419,
+          "test": false,
+          "traffic": false,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 339.7833333333333,
+          "penaltySeconds": 110,
+          "rescues": 0,
+          "contacts": 108
+        },
+        {
+          "seed": 509,
+          "test": false,
+          "traffic": false,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 370.2,
+          "penaltySeconds": 110,
+          "rescues": 0,
+          "contacts": 184
+        },
+        {
+          "seed": 601,
+          "test": false,
+          "traffic": false,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 268.01666666666665,
+          "penaltySeconds": 80,
+          "rescues": 0,
+          "contacts": 32
+        },
+        {
+          "seed": 211,
+          "test": false,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 192.35,
+          "penaltySeconds": 20,
+          "rescues": 2,
+          "contacts": 7136
+        },
+        {
+          "seed": 307,
+          "test": false,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 203.85,
+          "penaltySeconds": 23,
+          "rescues": 3,
+          "contacts": 7671
+        },
+        {
+          "seed": 419,
+          "test": false,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 201.83333333333334,
+          "penaltySeconds": 21,
+          "rescues": 3,
+          "contacts": 7779
+        },
+        {
+          "seed": 509,
+          "test": false,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 201.85,
+          "penaltySeconds": 20,
+          "rescues": 2,
+          "contacts": 6857
+        },
+        {
+          "seed": 601,
+          "test": false,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 159.21666666666667,
+          "penaltySeconds": 13,
+          "rescues": 1,
+          "contacts": 5746
+        },
+        {
+          "seed": 211,
+          "test": true,
+          "traffic": false,
+          "completed": false,
+          "success": false,
+          "laps": 1,
+          "finishSeconds": null,
+          "penaltySeconds": 138,
+          "rescues": 0,
+          "contacts": 140
+        },
+        {
+          "seed": 307,
+          "test": true,
+          "traffic": false,
+          "completed": false,
+          "success": false,
+          "laps": 1,
+          "finishSeconds": null,
+          "penaltySeconds": 132,
+          "rescues": 0,
+          "contacts": 222
+        },
+        {
+          "seed": 419,
+          "test": true,
+          "traffic": false,
+          "completed": false,
+          "success": false,
+          "laps": 1,
+          "finishSeconds": null,
+          "penaltySeconds": 144,
+          "rescues": 0,
+          "contacts": 370
+        },
+        {
+          "seed": 509,
+          "test": true,
+          "traffic": false,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 342.9,
+          "penaltySeconds": 98,
+          "rescues": 0,
+          "contacts": 14
+        },
+        {
+          "seed": 601,
+          "test": true,
+          "traffic": false,
+          "completed": false,
+          "success": false,
+          "laps": 2,
+          "finishSeconds": null,
+          "penaltySeconds": 158,
+          "rescues": 0,
+          "contacts": 379
+        },
+        {
+          "seed": 211,
+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 234.06666666666666,
+          "penaltySeconds": 33,
+          "rescues": 3,
+          "contacts": 7486
+        },
+        {
+          "seed": 307,
+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 321.6,
+          "penaltySeconds": 53,
+          "rescues": 1,
+          "contacts": 5791
+        },
+        {
+          "seed": 419,
+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 303.1,
+          "penaltySeconds": 62,
+          "rescues": 6,
+          "contacts": 8268
+        },
+        {
+          "seed": 509,
+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 146.31666666666666,
+          "penaltySeconds": 17,
+          "rescues": 1,
+          "contacts": 3703
+        },
+        {
+          "seed": 601,
+          "test": true,
+          "traffic": true,
+          "completed": true,
+          "success": false,
+          "laps": 3,
+          "finishSeconds": 267.45,
+          "penaltySeconds": 24,
+          "rescues": 2,
+          "contacts": 10415
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/packages/demo-car-circuit/src/racing/RaceGarage.tsx b/packages/demo-car-circuit/src/racing/RaceGarage.tsx
new file mode 100644
index 0000000..c93bda3
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/RaceGarage.tsx
@@ -0,0 +1,233 @@
+import { useEffect, useState } from "react";
+import * as tf from "@tensorflow/tfjs";
+import { LearnedRace, type RaceEntry } from "./learned-race";
+import { type SavedDriver } from "./training";
+import { loadGarage } from "./garage";
+const backendReady = tf.setBackend("cpu");
+
+export default function RaceGarage({
+  lang,
+  onStart,
+  onPractice,
+  onConfigure,
+  initialDriverId,
+}: {
+  lang: "fr" | "en";
+  onStart: (race: LearnedRace) => void;
+  onPractice: (references: boolean) => void;
+  onConfigure: () => void;
+  initialDriverId?: string;
+}) {
+  const fr = lang === "fr";
+  const [drivers, setDrivers] = useState<SavedDriver[]>([]),
+    [error, setError] = useState(""),
+    [ready, setReady] = useState(false);
+  const [human, setHuman] = useState(false);
+  const [humanModel, setHumanModel] = useState<"race" | "sedan-sports">("race");
+  const [slots, setSlots] = useState([initialDriverId ?? "bundled-11", "bundled-29"]);
+  const [models, setModels] = useState<("race" | "sedan-sports")[]>([
+    "race",
+    "sedan-sports",
+    "race",
+    "sedan-sports",
+  ]);
+  useEffect(() => {
+    let mounted = true;
+    void (async () => {
+      await backendReady;
+      const garage = await loadGarage({
+        storage: localStorage,
+        base: import.meta.env.BASE_URL,
+      });
+      if (mounted) {
+        setDrivers(garage.drivers);
+        setError(garage.errors.join(" · "));
+        setReady(true);
+      }
+    })().catch((e) => {
+      if (mounted) setError(String(e));
+    });
+    return () => {
+      mounted = false;
+    };
+  }, [fr]);
+  const start = () => {
+    try {
+      const entries: RaceEntry[] = slots.map((id, i) => {
+        const saved = drivers.find((d) => d.id === id);
+        if (!saved)
+          throw new Error(
+            fr ? "Choisissez un pilote valide." : "Choose a valid driver.",
+          );
+        return {
+          id: saved.id,
+          name: saved.name,
+          model: models[i],
+          checkpoint: saved.checkpoint,
+        };
+      });
+      const session = new LearnedRace(entries, human, false, humanModel);
+      onStart(session);
+      setError("");
+      (document.activeElement as HTMLElement)?.blur();
+    } catch (e) {
+      setError(String(e));
+    }
+  };
+  return (
+    <div className="training-panel" onFocusCapture={onConfigure}>
+      <div className="eyebrow">
+        {fr ? "LA GRILLE DE DÉPART" : "THE STARTING GRID"}
+      </div>
+      <h1>
+        {fr ? "Les pilotes\nentrent en piste." : "Drivers take\nthe track."}
+      </h1>
+      <p className="race-intro">
+        {human
+          ? fr
+            ? "Affrontez 1 à 3 pilotes entraînés. Votre voiture utilise exactement la même physique."
+            : "Race 1–3 trained drivers. Your car uses exactly the same physics."
+          : fr
+            ? "Choisissez 2 à 4 checkpoints. Chaque voiture utilise sa propre copie figée du réseau appris."
+            : "Choose 2–4 checkpoints. Every car uses its own frozen copy of the learned network."}
+      </p>
+      <p className="ghost-notice">{fr ? "Mode fantôme : les voitures se traversent sans se bloquer. Les barrières restent solides." : "Ghost mode: cars pass through each other without blocking. Track barriers remain solid."}</p>
+      <label>
+        {fr ? "Mode" : "Mode"}
+        <select
+          value={human ? "human" : "ai"}
+          onChange={(e) => {
+            const isHuman = e.target.value === "human";
+            setHuman(isHuman);
+            setSlots(current => isHuman ? [current[0]] : [current[0], "bundled-29"]);
+          }}
+        >
+          <option value="ai">{fr ? "Course IA" : "AI race"}</option>
+          <option value="human">
+            {fr ? "Jouer contre les IA" : "Play against AI"}
+          </option>
+        </select>
+      </label>
+      {human && (
+        <>
+          <label>
+            {fr ? "Votre véhicule" : "Your vehicle"}
+            <select
+              value={humanModel}
+              onChange={(e) =>
+                setHumanModel(e.target.value as "race" | "sedan-sports")
+              }
+            >
+              <option value="race">Formule R</option>
+              <option value="sedan-sports">Sport GT</option>
+            </select>
+          </label>
+          <p className="race-intro">
+            {fr
+              ? "↑ / Z / W : accélérer · ↓ / S : freiner · ← → / Q D : tourner · Échap : pause. Les sorties coûtent 2 s. Rester dans un rayon de 1 m pendant 5 s déclenche une remise (+5 s)."
+              : "↑ / W: accelerate · ↓ / S: brake · ← → / A D: steer · Escape: pause. Off-road costs 2 s. Staying within 1 m for 5 s triggers a rescue (+5 s)."}
+          </p>
+        </>
+      )}
+      <label>
+        {human
+          ? fr
+            ? "Adversaires"
+            : "Opponents"
+          : fr
+            ? "Nombre de pilotes"
+            : "Driver count"}
+        <select
+          value={slots.length}
+          onChange={(e) =>
+            setSlots(
+              Array.from(
+                { length: Number(e.target.value) },
+                (_, i) => slots[i] ?? `bundled-${[11, 29, 47][i % 3]}`,
+              ),
+            )
+          }
+        >
+          {(human ? [1, 2, 3] : [2, 3, 4]).map((n) => (
+            <option key={n} value={n}>
+              {n}
+            </option>
+          ))}
+        </select>
+      </label>
+      {slots.map((id, i) => {
+        const selected = drivers.find((d) => d.id === id);
+        return (
+          <div className="grid-driver" key={i}>
+            <label>
+              {fr ? "Pilote" : "Driver"} {i + 1}
+              <select
+                disabled={!ready}
+                value={drivers.some((d) => d.id === id) ? id : ""}
+                onChange={(e) =>
+                  setSlots(slots.map((v, j) => (j === i ? e.target.value : v)))
+                }
+              >
+                <option value="">
+                  {fr ? "Choisir un pilote" : "Choose a driver"}
+                </option>
+                {drivers.map((d) => (
+                  <option key={d.id} value={d.id}>
+                    {d.name}
+                  </option>
+                ))}
+              </select>
+            </label>
+            <label>
+              {fr ? "Véhicule" : "Vehicle"} {i + 1}
+              <select
+                value={models[i]}
+                onChange={(e) =>
+                  setModels(
+                    models.map((v, j) =>
+                      j === i ? (e.target.value as "race" | "sedan-sports") : v,
+                    ),
+                  )
+                }
+              >
+                <option value="race">Formule R</option>
+                <option value="sedan-sports">Sport GT</option>
+              </select>
+            </label>
+            {selected && (
+              <small>
+                {selected.reports.filter((r) => r.success).length}/
+                {selected.reports.length}{" "}
+                {fr ? "évaluations avec contacts réussies" : "successful contact evaluations"} ·{" "}
+                {selected.id}
+              </small>
+            )}
+          </div>
+        );
+      })}
+      <div className="training-actions">
+        <button className="drive-button" disabled={!ready} onClick={start}>
+          {human
+            ? fr
+              ? "Prendre le départ"
+              : "Start racing"
+            : fr
+              ? "Lancer la course IA"
+              : "Start AI race"}
+        </button>
+      </div>
+      <p className="race-intro">
+        {fr
+          ? "Les pilotes fournis sont imparfaits : leurs échecs d’évaluation restent visibles. Aucun contrôleur à règles ne les remplace."
+          : "Bundled drivers are imperfect: evaluation failures remain visible. No rule controller replaces them."}
+      </p>
+      <button className="reset-button" onClick={() => onPractice(false)}>
+        {fr ? "Course solo au clavier" : "Solo keyboard race"}
+      </button>
+      <button className="reset-button" onClick={() => onPractice(true)}>
+        {fr ? "Observer les références à règles" : "Watch rule references"}
+      </button>
+      {error && <p role="alert">{error}</p>}
+    </div>
+  );
+}
diff --git a/packages/demo-car-circuit/src/racing/RaceMap.tsx b/packages/demo-car-circuit/src/racing/RaceMap.tsx
new file mode 100644
index 0000000..56eb97c
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/RaceMap.tsx
@@ -0,0 +1,34 @@
+import { RaceWorld } from "./race";
+/** Small live map also identifies overlapping cars without changing simulation state. */
+export function RaceMap({ race, follow }: { race: RaceWorld; follow: number }) {
+  const path = race.track.points.map((p) => `${p.x},${p.z}`).join(" ");
+  return (
+    <svg
+      className="race-map"
+      viewBox="-80 -55 160 110"
+      role="img"
+      aria-label="Circuit map"
+    >
+      <polyline points={path} fill="none" stroke="#74848a" strokeWidth="7" />
+      <polyline points={path} fill="none" stroke="#18242a" strokeWidth="4" />
+      {race.drivers.map((d) => (
+        <g key={d.id}>
+          <circle
+            cx={d.world.car.x}
+            cy={d.world.car.z}
+            r={d.id === follow ? 4 : 3}
+            fill={d.id === follow ? "#e6ee58" : "#f3f1e8"}
+          />
+          <text
+            x={d.world.car.x + 4}
+            y={d.world.car.z - 4}
+            fill="white"
+            fontSize="7"
+          >
+            {d.id + 1}
+          </text>
+        </g>
+      ))}
+    </svg>
+  );
+}
diff --git a/packages/demo-car-circuit/src/racing/RacingApp.tsx b/packages/demo-car-circuit/src/racing/RacingApp.tsx
new file mode 100644
index 0000000..12697e2
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/RacingApp.tsx
@@ -0,0 +1,525 @@
+import {
+  lazy,
+  Suspense,
+  Component,
+  useEffect,
+  useRef,
+  useState,
+  type ReactNode,
+} from "react";
+import { useProgress } from "@react-three/drei";
+import { RaceWorld } from "./race";
+import { DrivingWorld } from "./driving";
+import { RaceMap } from "./RaceMap";
+import { RacingScene } from "./RacingScene";
+import "./racing.css";
+
+import type { LearnedRace } from "./learned-race";
+const RaceGarage = lazy(() => import("./RaceGarage"));
+const TrainingPanel = lazy(() => import("./TrainingPanel"));
+
+const copy = {
+  fr: {
+    garage: "Garage",
+    drive: "Essais libres",
+    training: "Entraîner",
+    race: "Course",
+    tag: "LE LABORATOIRE DES PILOTES",
+    title: "Votre voiture.\nVotre trajectoire.",
+    intro:
+      "Prenez le volant sur Alpine Park. Familiarisez-vous avec le circuit avant d’entraîner votre premier pilote.",
+    vehicle: "VÉHICULE",
+    racecar: "Formule R",
+    sedan: "Sport GT",
+    equal: "Même physique. Deux caractères.",
+    start: "Prendre le volant",
+    pause: "Pause",
+    resume: "Reprendre",
+    reset: "Retour aux stands",
+    controls: "COMMANDES",
+    gas: "Accélérer",
+    brake: "Freiner",
+    steer: "Tourner",
+    track: "ALPINE PARK",
+    layout: "10 virages · Circuit école",
+    mode: "ESSAIS LIBRES",
+    hint: "Accélérez avec ↑ ou Z. Freinez avant le virage.",
+    loading: "Préparation du véhicule…",
+    error: "Le véhicule n’a pas pu être chargé.",
+    retry: "Réessayer",
+    coming: "Disponible après les essais",
+    paused: "Session en pause",
+    pausedHint: "Reprenez quand vous êtes prêt.",
+    speed: "VITESSE",
+    local: "LOCAL · NAVIGATEUR",
+  },
+  en: {
+    garage: "Garage",
+    drive: "Free practice",
+    training: "Train",
+    race: "Race",
+    tag: "THE DRIVER LAB",
+    title: "Your car.\nYour racing line.",
+    intro:
+      "Take the wheel at Alpine Park. Learn the circuit before training your first driver.",
+    vehicle: "VEHICLE",
+    racecar: "Formula R",
+    sedan: "Sport GT",
+    equal: "Same physics. Two characters.",
+    start: "Start driving",
+    pause: "Pause",
+    resume: "Resume",
+    reset: "Back to pits",
+    controls: "CONTROLS",
+    gas: "Accelerate",
+    brake: "Brake",
+    steer: "Steer",
+    track: "ALPINE PARK",
+    layout: "10 corners · Training circuit",
+    mode: "FREE PRACTICE",
+    hint: "Accelerate with ↑ or W. Brake before the corner.",
+    loading: "Preparing your vehicle…",
+    error: "The vehicle could not be loaded.",
+    retry: "Retry",
+    coming: "Available after practice",
+    paused: "Session paused",
+    pausedHint: "Resume when you are ready.",
+    speed: "SPEED",
+    local: "LOCAL · BROWSER",
+  },
+};
+class SceneBoundary extends Component<
+  { children: ReactNode; message: string; retry: string },
+  { error: boolean }
+> {
+  state = { error: false };
+  static getDerivedStateFromError() {
+    return { error: true };
+  }
+  render() {
+    return this.state.error ? (
+      <div className="race-error">
+        <p>{this.props.message}</p>
+        <button onClick={() => location.reload()}>{this.props.retry}</button>
+      </div>
+    ) : (
+      this.props.children
+    );
+  }
+}
+function Loading({ label }: { label: string }) {
+  const { active, progress } = useProgress();
+  return active ? (
+    <div className="race-loading" role="status">
+      <span>{label}</span>
+      <progress max={100} value={progress} />
+    </div>
+  ) : null;
+}
+export default function RacingApp() {
+  const [lang, setLang] = useState<"fr" | "en">("fr"),
+    t = copy[lang];
+  const [world, setWorld] = useState(() => new DrivingWorld());
+  const [race, setRace] = useState<RaceWorld>();
+  const [reference, setReference] = useState(false);
+  const [trainingMode, setTrainingMode] = useState(false);
+  const [garageMode, setGarageMode] = useState(false);
+  const [preferredDriver, setPreferredDriver] = useState<string>();
+  const [learned, setLearned] = useState<LearnedRace>();
+  const [follow, setFollow] = useState(0);
+  const displayedDriver = race?.drivers[learned ? follow : 0];
+  useEffect(() => () => learned?.dispose(), [learned]);
+  const [, updateHUD] = useState(0);
+  const resetSession = (competitive: boolean, references = false) => {
+    setTrainingMode(false);
+    setGarageMode(false);
+    setLearned(undefined);
+    setFollow(0);
+    const next = competitive
+      ? new RaceWorld({ count: references ? 4 : 1, ghost: true })
+      : undefined;
+    setReference(references);
+    setRace(next);
+    setWorld(next ? next.drivers[0].world : new DrivingWorld());
+    keys.clear();
+    setSpeed(0);
+    setPaused(true);
+    setStarted(false);
+  };
+  const keys = useRef(new Set<string>()).current;
+  const [started, setStarted] = useState(false),
+    [paused, setPaused] = useState(true),
+    [model, setModel] = useState("race"),
+    [speed, setSpeed] = useState(0);
+  useEffect(() => {
+    const handled = new Set([
+      "ArrowUp",
+      "ArrowDown",
+      "ArrowLeft",
+      "ArrowRight",
+      "KeyW",
+      "KeyA",
+      "KeyS",
+      "KeyD",
+      "KeyZ",
+      "KeyQ",
+    ]);
+    const down = (e: KeyboardEvent) => {
+      if (e.code === "Escape") {
+        keys.clear();
+        setPaused(true);
+        return;
+      }
+      if (
+        e.target instanceof HTMLElement &&
+        ["INPUT", "SELECT", "TEXTAREA", "BUTTON"].includes(e.target.tagName)
+      )
+        return;
+      if (handled.has(e.code)) {
+        e.preventDefault();
+        keys.add(e.code);
+      }
+    };
+    const up = (e: KeyboardEvent) => keys.delete(e.code);
+    const blur = () => {
+      keys.clear();
+      setPaused(true);
+    };
+    window.addEventListener("keydown", down);
+    window.addEventListener("keyup", up);
+    window.addEventListener("blur", blur);
+    document.addEventListener("visibilitychange", blur);
+    return () => {
+      window.removeEventListener("keydown", down);
+      window.removeEventListener("keyup", up);
+      window.removeEventListener("blur", blur);
+      document.removeEventListener("visibilitychange", blur);
+      keys.clear();
+    };
+  }, [keys]);
+  return (
+    <div className="racing-app">
+      <header className="race-header">
+        <a href="/" className="race-brand">
+          ignition<span> / RACING</span>
+        </a>
+        <nav aria-label="Modes">
+          <button
+            className={!race && !trainingMode && !garageMode ? "active" : ""}
+            onClick={() => resetSession(false)}
+          >
+            {t.drive}
+          </button>
+          <button
+            className={trainingMode ? "active" : ""}
+            onClick={() => {
+              keys.clear();
+              setPaused(true);
+              setRace(undefined);
+              setStarted(false);
+              setTrainingMode(true);
+              setGarageMode(false);
+              setLearned(undefined);
+              setFollow(0);
+            }}
+          >
+            {t.training}
+          </button>
+          <button
+            className={race || garageMode ? "active" : ""}
+            onClick={() => {
+              keys.clear();
+              setPaused(true);
+              setTrainingMode(false);
+              setGarageMode(true);
+            }}
+          >
+            {t.race}
+          </button>
+        </nav>
+        <button
+          className="language"
+          onClick={() => {
+            keys.clear();
+            setPaused(true);
+            setLang(lang === "fr" ? "en" : "fr");
+          }}
+        >
+          {lang.toUpperCase()} ↔
+        </button>
+      </header>
+      <main className="race-layout">
+        <aside className="race-sidebar">
+          {garageMode ? (
+            <Suspense fallback={<p>{t.loading}</p>}>
+              <RaceGarage
+                initialDriverId={preferredDriver}
+                onConfigure={() => {
+                  keys.clear();
+                  setPaused(true);
+                }}
+                lang={lang}
+                onPractice={(r) => resetSession(true, r)}
+                onStart={(session) => {
+                  keys.clear();
+                  setLearned(session);
+                  setRace(session.race);
+                  setWorld(session.race.drivers[0].world);
+                  setFollow(0);
+                  setStarted(true);
+                  setPaused(false);
+                  setReference(false);
+                }}
+              />
+            </Suspense>
+          ) : trainingMode ? (
+            <Suspense fallback={<p>{t.loading}</p>}>
+              <TrainingPanel lang={lang} onWorld={setWorld} onRace={(id) => { setPreferredDriver(id); setTrainingMode(false); setGarageMode(true); setWorld(new DrivingWorld()); setSpeed(0); setPaused(true); keys.clear(); }} />
+            </Suspense>
+          ) : (
+            <>
+              <div className="eyebrow">{t.tag}</div>
+              <h1>
+                {t.title.split("\n").map((s, i) => (
+                  <span key={i}>
+                    {s}
+                    <br />
+                  </span>
+                ))}
+              </h1>
+              <p className="race-intro">{t.intro}</p>
+              <div className="vehicle-label">{t.vehicle}</div>
+              <div className="vehicle-options">
+                {[
+                  ["race", t.racecar, "01"],
+                  ["sedan-sports", t.sedan, "02"],
+                ].map(([id, label, index]) => (
+                  <button
+                    key={id}
+                    className={model === id ? "selected" : ""}
+                    onClick={() => {
+                      keys.clear();
+                      setPaused(true);
+                      setModel(id);
+                    }}
+                    aria-pressed={model === id}
+                  >
+                    <span>{index}</span>
+                    {label}
+                    <b>{model === id ? "↗" : "+"}</b>
+                  </button>
+                ))}
+              </div>
+              <p className="muted">{t.equal}</p>
+              <button
+                className="drive-button"
+                onClick={() => {
+                  keys.clear();
+                  setStarted(true);
+                  setPaused(!paused);
+                  (document.activeElement as HTMLElement)?.blur();
+                }}
+              >
+                {!started ? t.start : paused ? t.resume : t.pause}
+                <span>↗</span>
+              </button>
+              <button
+                className="reset-button"
+                onClick={() => {
+                  keys.clear();
+                  resetSession(!!race, reference);
+                }}
+              >
+                {t.reset}
+              </button>
+              {race && (
+                <button
+                  className="reset-button"
+                  onClick={() => resetSession(true, !reference)}
+                >
+                  {reference
+                    ? lang === "fr"
+                      ? "Piloter en solo"
+                      : "Drive solo"
+                    : lang === "fr"
+                      ? "Observer 4 références à règles"
+                      : "Watch 4 rule-based references"}
+                </button>
+              )}
+              <div className="race-controls">
+                <div className="vehicle-label">{t.controls}</div>
+                <p>
+                  <kbd>↑ / Z / W</kbd>
+                  <span>{t.gas}</span>
+                </p>
+                <p>
+                  <kbd>↓ / S</kbd>
+                  <span>{t.brake}</span>
+                </p>
+                <p>
+                  <kbd>← →</kbd>
+                  <span>{t.steer}</span>
+                </p>
+                <p>
+                  <kbd>ESC</kbd>
+                  <span>{t.pause}</span>
+                </p>
+              </div>
+              <div className="local-badge">
+                <i />
+                {t.local}
+              </div>
+            </>
+          )}
+        </aside>
+        <section className="race-viewport" aria-label={t.track}>
+          <SceneBoundary message={t.error} retry={t.retry}>
+            <RacingScene
+              world={learned ? learned.race.drivers[follow].world : world}
+              keys={keys}
+              paused={paused}
+              model={model}
+              race={race}
+              reference={reference}
+              learned={learned}
+              onStats={(value) => {
+                setSpeed(value);
+                updateHUD((n) => n + 1);
+              }}
+            />
+          </SceneBoundary>
+          <Loading label={t.loading} />
+          {race && <RaceMap race={race} follow={learned ? follow : 0} />}
+          {learned && (
+            <div className="spectator-controls">
+              <label>
+                {lang === "fr" ? "Caméra" : "Camera"}
+                <select
+                  disabled={learned.human}
+                  value={follow}
+                  onChange={(e) => setFollow(Number(e.target.value))}
+                >
+                  {learned.competitors.map((entry, i) => (
+                    <option key={i} value={i}>
+                      {entry.id === "player" ? (lang === "fr" ? "Vous" : "You") : entry.name}
+                    </option>
+                  ))}
+                </select>
+              </label>
+              <button onClick={() => {keys.clear();setPaused(!paused);(document.activeElement as HTMLElement)?.blur();}}>
+                {paused ? t.resume : t.pause}
+              </button>
+            </div>
+          )}
+
+          <div className="track-label">
+            <span>{t.track}</span>
+            <small>{trainingMode ? (lang === "fr" ? "Aperçu par session · apprentissage accéléré" : "Session snapshot · accelerated learning") : t.layout}</small>
+          </div>
+          <div className="session-pill">
+            <i />
+            {race
+              ? `${reference ? (lang === "fr" ? "RÉFÉRENCES À RÈGLES" : "RULE-BASED REFERENCES") : t.race} · ${Math.min(3, race.drivers[learned ? follow : 0].laps + 1)}/3`
+              : trainingMode
+                ? t.training
+                : t.mode}
+          </div>
+          {race && started && !paused && race.countdown > 0 && (
+            <div className="race-countdown" role="status">
+              {Math.ceil(race.countdown)}
+            </div>
+          )}
+          {race && (
+            <div className="race-timing">
+              {race.standings.findIndex(
+                (d) => d.id === (learned ? follow : 0),
+              ) + 1}
+              /{race.drivers.length} · {(race.elapsedTicks / 60).toFixed(1)} s ·
+              +{race.drivers[learned ? follow : 0].penaltySeconds} s
+              <br />
+              <span>{lang === "fr" ? "Tour" : "Lap"} {Math.min(3, race.drivers[learned ? follow : 0].laps + 1)}/3</span>
+              {race.ghost && <small className="ghost-race-label">{lang === "fr" ? "MODE FANTÔME" : "GHOST MODE"}</small>}
+            </div>
+          )}
+          {race?.finished && displayedDriver && (
+            <div className="pause-overlay">
+              <h2>{lang === "fr" ? "Résultat" : "Result"}</h2>
+              {race.ghost && <p>{lang === "fr" ? "Course en mode fantôme" : "Ghost race"}</p>}
+              {learned && <p>{learned.competitors[follow].name}</p>}
+              <p>
+                {displayedDriver.finishSeconds === null
+                  ? lang === "fr"
+                    ? "Temps limite atteint"
+                    : "Time limit reached"
+                  : `${displayedDriver.finishSeconds.toFixed(2)} s · 3/3`}
+              </p>
+              <p>
+                {lang === "fr" ? "Remises en piste" : "Rescues"}:{" "}
+                {displayedDriver.rescues} (+
+                {race.drivers[learned ? follow : 0].penaltySeconds} s)
+              </p>
+              <ol>
+                {race.standings.map((d) => (
+                  <li key={d.id}>
+                    <span className="result-driver">
+                      {learned?.competitors[d.id]?.name ?? `#${d.id + 1}`}
+                      {learned && <small>{learned.competitors[d.id].id}</small>}
+                    </span>
+                    <span className="result-time">{d.finishSeconds === null ? "DNF" : `${d.finishSeconds.toFixed(2)} s`}</span>
+                  </li>
+                ))}
+              </ol>
+              <button
+                onClick={() => {
+                  if (learned) {
+                    setGarageMode(true);
+                    setPaused(true);
+                    setStarted(false);
+                    setLearned(undefined);
+                    setRace(undefined);
+                  } else resetSession(true, reference);
+                }}
+              >
+                {t.reset}
+              </button>
+            </div>
+          )}
+          {started && paused && !race?.finished && (
+            <div className="pause-overlay">
+              <h2>{t.paused}</h2>
+              <p>{t.pausedHint}</p>
+              <button
+                onClick={() => {
+                  keys.clear();
+                  setPaused(false);
+                  (document.activeElement as HTMLElement)?.blur();
+                }}
+              >
+                {t.resume} →
+              </button>
+            </div>
+          )}
+          <div className="driving-hint">
+            {learned && !learned.human
+              ? lang === "fr"
+                ? "Course de réseaux appris · poids figés"
+                : "Learned networks racing · frozen weights"
+              : trainingMode
+                ? lang === "fr"
+                  ? "Trajectoires collectées pendant l’apprentissage"
+                  : "Trajectories collected during learning"
+                : t.hint}
+          </div>
+          <div className="speedometer">
+            <span>{t.speed}</span>
+            <strong>{Math.round(speed).toString().padStart(3, "0")}</strong>
+            <small>KM/H</small>
+            <div className="speed-track">
+              <i style={{ width: `${(speed / 115) * 100}%` }} />
+            </div>
+          </div>
+        </section>
+      </main>
+    </div>
+  );
+}
diff --git a/packages/demo-car-circuit/src/racing/RacingScene.tsx b/packages/demo-car-circuit/src/racing/RacingScene.tsx
new file mode 100644
index 0000000..bc80633
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/RacingScene.tsx
@@ -0,0 +1,263 @@
+import { Suspense, useEffect, useMemo, useRef } from "react";
+import { Canvas, useFrame } from "@react-three/fiber";
+import { useGLTF, useProgress } from "@react-three/drei";
+import * as THREE from "three";
+import type { LearnedRace } from "./learned-race";
+import { referenceAction } from "./reference";
+import { RaceWorld } from "./race";
+import { DrivingWorld, DRIVING_CONTRACT, RacingTrack } from "./driving";
+
+export function Vehicle({
+  world,
+  model,
+}: {
+  world: DrivingWorld;
+  model: string;
+}) {
+  const { scene } = useGLTF(`${import.meta.env.BASE_URL}models/${model}.glb`);
+  const group = useRef<THREE.Group>(null);
+  const object = useMemo(() => {
+    const copy = scene.clone(true),
+      box = new THREE.Box3().setFromObject(copy),
+      size = box.getSize(new THREE.Vector3());
+    const scale = 4.4 / Math.max(size.x, size.z);
+    copy.scale.setScalar(scale);
+    const center = box.getCenter(new THREE.Vector3());
+    copy.position.set(-center.x * scale, -box.min.y * scale, -center.z * scale);
+    copy.traverse((o) => {
+      if (o instanceof THREE.Mesh) {
+        o.castShadow = true;
+        o.receiveShadow = true;
+      }
+    });
+    return copy;
+  }, [scene]);
+  useFrame(() => {
+    if (group.current) {
+      const c = world.car;
+      group.current.position.set(c.x, 0.04, c.z);
+      group.current.rotation.y = -c.angle + Math.PI / 2;
+    }
+  });
+  return (
+    <group ref={group}>
+      <primitive object={object} />
+    </group>
+  );
+}
+function Road({ track }: { track: RacingTrack }) {
+  const geometry = useMemo(() => {
+    const positions: number[] = [],
+      colors: number[] = [];
+    for (let i = 0; i < 400; i++) {
+      const a = track.sample(i / 400),
+        b = track.sample((i + 1) / 400);
+      for (const [l, r, color] of [
+        [-5.3, -4.5, i % 8 < 4 ? "#f4efdf" : "#d74332"],
+        [-4.5, 4.5, "#30383b"],
+        [4.5, 5.3, i % 8 < 4 ? "#f4efdf" : "#d74332"],
+      ] as [number, number, string][]) {
+        const v = (p: typeof a, s: number) => [
+          p.x - Math.sin(p.angle) * s,
+          0.02,
+          p.z + Math.cos(p.angle) * s,
+        ];
+        const verts = [v(a, l), v(a, r), v(b, r), v(a, l), v(b, r), v(b, l)],
+          c = new THREE.Color(color);
+        verts.forEach((p) => {
+          positions.push(...p);
+          colors.push(c.r, c.g, c.b);
+        });
+      }
+    }
+    const g = new THREE.BufferGeometry();
+    g.setAttribute("position", new THREE.Float32BufferAttribute(positions, 3));
+    g.setAttribute("color", new THREE.Float32BufferAttribute(colors, 3));
+    g.computeVertexNormals();
+    return g;
+  }, [track]);
+  useEffect(() => () => geometry.dispose(), [geometry]);
+  return (
+    <>
+      <mesh geometry={geometry} receiveShadow>
+        <meshStandardMaterial
+          vertexColors
+          side={THREE.DoubleSide}
+          roughness={0.94}
+        />
+      </mesh>
+      <Rails track={track} />
+      {Array.from({ length: 10 }, (_, i) => {
+        const p = track.sample(0);
+        return (
+          <mesh
+            key={i}
+            position={[
+              p.x - Math.sin(p.angle) * (i - 4.5) * 0.9,
+              0.04,
+              p.z + Math.cos(p.angle) * (i - 4.5) * 0.9,
+            ]}
+            rotation={[0, -p.angle, 0]}
+          >
+            <boxGeometry args={[1.1, 0.025, 0.9]} />
+            <meshStandardMaterial color={i % 2 ? "#111" : "#fff"} />
+          </mesh>
+        );
+      })}
+    </>
+  );
+}
+/** Continuous rails share their offset and thickness with the collision contract. */
+function Rails({ track }: { track: RacingTrack }) {
+  const geometry = useMemo(() => {
+    const positions: number[] = [];
+    const { barrierOffset, barrierThickness } = DRIVING_CONTRACT;
+    for (const side of [-1, 1]) for (let i = 0; i < 400; i++) {
+      const endpoints = [track.sample(i / 400), track.sample((i + 1) / 400)];
+      const vertices = endpoints.flatMap(p => [0, 1].flatMap(y => [-1, 1].map(edge => {
+        const offset = side * barrierOffset + edge * barrierThickness / 2;
+        return [p.x - Math.sin(p.angle) * offset, y, p.z + Math.cos(p.angle) * offset];
+      })));
+      for (const face of [[0,4,6,2], [1,3,7,5], [2,6,7,3]]) {
+        for (const index of [face[0],face[1],face[2],face[0],face[2],face[3]]) positions.push(...vertices[index]);
+      }
+    }
+    const g = new THREE.BufferGeometry();
+    g.setAttribute("position", new THREE.Float32BufferAttribute(positions, 3));
+    g.computeVertexNormals();
+    return g;
+  }, [track]);
+  useEffect(() => () => geometry.dispose(), [geometry]);
+  return <mesh geometry={geometry} castShadow receiveShadow><meshStandardMaterial color="#ced3ca" side={THREE.DoubleSide} /></mesh>;
+}
+function Driver({
+  world,
+  keys,
+  paused,
+  onStats,
+  race,
+  reference = false,
+  learned,
+}: {
+  world: DrivingWorld;
+  keys: Set<string>;
+  paused: boolean;
+  onStats: (speed: number) => void;
+  race?: RaceWorld;
+  reference?: boolean;
+  learned?: LearnedRace;
+}) {
+  const { active: loading } = useProgress();
+  const accumulator = useRef(0),
+    frames = useRef(0),
+    cameraReady = useRef(false);
+  useFrame(({ camera }, delta) => {
+    if (!paused && !loading) {
+      accumulator.current += Math.min(delta, 0.1);
+      while (accumulator.current >= DRIVING_CONTRACT.dt) {
+        const steer =
+          (keys.has("ArrowRight") || keys.has("KeyD") ? 1 : 0) -
+          (keys.has("ArrowLeft") || keys.has("KeyA") || keys.has("KeyQ")
+            ? 1
+            : 0);
+        const throttle =
+          keys.has("ArrowDown") || keys.has("KeyS")
+            ? -1
+            : keys.has("ArrowUp") || keys.has("KeyW") || keys.has("KeyZ")
+              ? 1
+              : 0;
+        const action = (throttle + 1) * 3 + steer + 1;
+        if (learned) learned.step(action);
+        else if (race)
+          race.step(
+            race.drivers.map((d, i) =>
+              reference || i > 0 ? referenceAction(d.world) : action,
+            ),
+          );
+        else world.step(action);
+        accumulator.current -= DRIVING_CONTRACT.dt;
+      }
+    } else accumulator.current = 0;
+    const c = world.car,
+      target = new THREE.Vector3(
+        c.x - Math.cos(c.angle) * 12,
+        7.5,
+        c.z - Math.sin(c.angle) * 12,
+      );
+    if (!cameraReady.current) {
+      camera.position.copy(target);
+      cameraReady.current = true;
+    } else camera.position.lerp(target, 1 - Math.exp(-delta * 5));
+    camera.lookAt(
+      c.x + Math.cos(c.angle) * 8,
+      0.8,
+      c.z + Math.sin(c.angle) * 8,
+    );
+    if (++frames.current % 6 === 0) onStats(c.speed * 3.6);
+  });
+  return null;
+}
+export function RacingScene(props: {
+  world: DrivingWorld;
+  keys: Set<string>;
+  paused: boolean;
+  model: string;
+  onStats: (speed: number) => void;
+  race?: RaceWorld;
+  reference?: boolean;
+  learned?: LearnedRace;
+}) {
+  return (
+    <Canvas shadows camera={{ fov: 55, near: 0.1, far: 600 }} dpr={[1, 1.5]}>
+      <color attach="background" args={["#a6c4c8"]} />
+      <fog attach="fog" args={["#a6c4c8", 100, 350]} />
+      <hemisphereLight args={["#e4f5ff", "#5c6949", 2]} />
+      <directionalLight
+        position={[30, 70, -25]}
+        intensity={3}
+        castShadow
+        shadow-mapSize={[2048, 2048]}
+        shadow-camera-left={-100}
+        shadow-camera-right={100}
+        shadow-camera-top={100}
+        shadow-camera-bottom={-100}
+        shadow-camera-far={200}
+      />
+      <mesh rotation={[-Math.PI / 2, 0, 0]} receiveShadow>
+        <planeGeometry args={[1000, 1000]} />
+        <meshStandardMaterial color="#6d805a" roughness={1} />
+      </mesh>
+      {Array.from({ length: 24 }, (_, i) => {
+        const a = (i * Math.PI * 2) / 24;
+        return (
+          <mesh
+            key={i}
+            position={[Math.cos(a) * 190, 0, Math.sin(a) * 180]}
+            scale={[1.4, 0.45, 1.2]}
+          >
+            <sphereGeometry args={[32 + (i % 4) * 7, 10, 6]} />
+            <meshStandardMaterial color={i % 2 ? "#677b6a" : "#7d8c71"} />
+          </mesh>
+        );
+      })}
+      <Road track={props.world.track} />
+      <Suspense fallback={null}>
+        {props.race ? (
+          props.race.drivers.map((d) => (
+            <Vehicle
+              key={d.id}
+              world={d.world}
+              model={
+                props.learned?.competitors[d.id]?.model ??
+                (d.id % 2 ? "sedan-sports" : props.model)
+              }
+            />
+          ))
+        ) : (
+          <Vehicle world={props.world} model={props.model} />
+        )}
+      </Suspense>
+      <Driver {...props} />
+    </Canvas>
+  );
+}
diff --git a/packages/demo-car-circuit/src/racing/TrainingPanel.tsx b/packages/demo-car-circuit/src/racing/TrainingPanel.tsx
new file mode 100644
index 0000000..f674c1a
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/TrainingPanel.tsx
@@ -0,0 +1,246 @@
+import * as tf from "@tensorflow/tfjs";
+import { useEffect, useRef, useState } from "react";
+import { trainQDriver } from "./q-training";
+import { Q_PROTOCOL } from "./q-protocol";
+import { LearnedDriver, type DriverCheckpoint } from "./learned-driver";
+import {
+  trainDriver,
+  evaluateDriver,
+  listDrivers,
+  saveDriver,
+  type SavedDriver,
+  type DriverEvaluation,
+} from "./training";
+import { DrivingWorld } from "./driving";
+
+const backendReady = tf.setBackend("cpu");
+
+export default function TrainingPanel({
+  lang,
+  onWorld,
+  onRace,
+}: {
+  lang: "fr" | "en";
+  onWorld: (world: DrivingWorld) => void;
+  onRace: (id: string) => void;
+}) {
+  const fr = lang === "fr";
+  const agent = useRef<LearnedDriver | undefined>(undefined);
+  const operation = useRef<Promise<void> | null>(null),
+    controller = useRef<AbortController | undefined>(undefined);
+  const [status, setStatus] = useState("idle"),
+    [error, setError] = useState(""),
+    [seed, setSeed] = useState(11);
+  const [progress, setProgress] = useState({
+    round: 0,
+    loss: null as number | null,
+    samples: 0,
+    traffic: false,
+  });
+  const [records, setRecords] = useState<SavedDriver[]>([]),
+    [reports, setReports] = useState<DriverEvaluation[]>([]);
+  const [selected, setSelected] = useState(""),
+    [saved, setSaved] = useState("");
+  const [algorithm, setAlgorithm] = useState<DriverCheckpoint["algorithm"]>("imitation-mlp");
+  const [savedId, setSavedId] = useState("");
+  const [ready, setReady] = useState(false);
+  const activeAlgorithm = agent.current?.algorithm ?? algorithm;
+  const busy = !ready || status === "training" || status === "evaluating";
+  useEffect(() => {
+    let mounted = true;
+    void backendReady
+      .then(() => {
+        if (mounted) setReady(true);
+      })
+      .catch((e) => {
+        if (mounted) setError(String(e));
+      });
+    try {
+      setRecords(listDrivers());
+    } catch (e) {
+      setError(String(e));
+    }
+    return () => {
+      mounted = false;
+      controller.current?.abort();
+      void (operation.current ?? Promise.resolve()).finally(() =>
+        agent.current?.dispose(),
+      );
+    };
+  }, []);
+  const start = (fresh: boolean) => {
+    if (busy) return;
+    if (fresh || !agent.current) {
+      agent.current?.dispose();
+      agent.current = new LearnedDriver(seed, algorithm);
+      setReports([]);
+      setSaved("");
+    }
+    setReports([]);
+    setSaved("");
+    setSavedId("");
+
+    const driver = agent.current;
+    setSeed(driver.seed);
+    setAlgorithm(driver.algorithm);
+    setProgress({round:0,loss:null,samples:driver.samples,traffic:driver.algorithm === "imitation-mlp" ? driver.updates>=24 : driver.samples>=Q_PROTOCOL.trafficAfterTransitions});
+    controller.current = new AbortController();
+    setError("");
+    setStatus("training");
+    const common = {
+      signal: controller.current.signal,
+      onWorld: (r: import("./race").RaceWorld) => onWorld(r.drivers[0].world),
+      onProgress: setProgress,
+    };
+    operation.current = (driver.algorithm === "imitation-mlp"
+      ? trainDriver(driver, { ...common, seed:driver.seed, rounds:16 })
+      : trainQDriver(driver.exportCheckpoint(), common).then(checkpoint => {
+        const updated = LearnedDriver.fromCheckpoint(checkpoint);
+        driver.dispose();
+        agent.current = updated;
+      }))
+      .catch((e) => setError(String(e)))
+      .finally(() => setStatus("idle"));
+  };
+  const evaluate = () => {
+    if (!agent.current || busy) return;
+    const frozen = LearnedDriver.fromCheckpoint(
+      agent.current.exportCheckpoint(),
+    );
+    controller.current = new AbortController();
+    setError("");
+    setStatus("evaluating");
+    setSavedId("");
+    setReports([]);
+    operation.current = (async () => {
+      const results: DriverEvaluation[] = [];
+      try {
+        for (const test of [false, true])
+          for (const traffic of [false, true])
+            for (const evaluationSeed of frozen.algorithm === "imitation-mlp" ? [101, 307, 509] : Q_PROTOCOL.evaluationSeeds) {
+              results.push(
+                await evaluateDriver(frozen, {
+                  seed: evaluationSeed,
+                  test,
+                  traffic,
+                  signal: controller.current!.signal,
+                }),
+              );
+              setReports([...results]);
+            }
+      } finally {
+        frozen.dispose();
+      }
+    })()
+      .catch((e) => {
+        if (!controller.current?.signal.aborted) setError(String(e));
+      })
+      .finally(() => setStatus("idle"));
+  };
+  const save = () => {
+    if (!agent.current || busy) return;
+    try {
+      const id = `driver-${agent.current.seed}-${Date.now()}`;
+      saveDriver({
+        id,
+        name: `${agent.current.algorithm} · ${agent.current.seed} · ${agent.current.updates}`,
+        checkpoint: agent.current.exportCheckpoint(),
+        reports,
+      });
+      setRecords(listDrivers());
+      setSelected(id);
+      setSavedId(id);
+      setSaved(
+        fr
+          ? "Pilote sauvegardé dans ce navigateur."
+          : "Driver saved in this browser.",
+      );
+      setError("");
+    } catch (e) {
+      setError(String(e));
+    }
+  };
+  const load = () => {
+    const record = records.find((r) => r.id === selected);
+    if (!record || busy) return;
+    try {
+      const loaded = LearnedDriver.fromCheckpoint(record.checkpoint);
+      agent.current?.dispose();
+      agent.current = loaded;
+      setSavedId(record.id);
+      setSeed(loaded.seed);
+      setAlgorithm(loaded.algorithm);
+      setReports(record.reports);
+      setProgress({
+        round: 0,
+        loss: null as number | null,
+        samples: loaded.samples,
+        traffic: loaded.algorithm === "imitation-mlp" ? loaded.updates >= 24 : loaded.samples >= Q_PROTOCOL.trafficAfterTransitions,
+      });
+      setError("");
+      setSaved(
+        fr
+          ? "Poids rechargés. La reprise recrée l’optimiseur."
+          : "Weights loaded. Resuming creates a fresh optimizer.",
+      );
+    } catch (e) {
+      setError(String(e));
+    }
+  };
+  const trained = !!agent.current?.samples;
+  const totalTests = activeAlgorithm === "imitation-mlp" ? 12 : 20;
+  const tested = reports.length === totalTests;
+  const budget = activeAlgorithm === "imitation-mlp" ? 16 : Q_PROTOCOL.transitions;
+  const step = savedId ? 4 : tested ? 3 : trained && status !== "training" ? 2 : 1;
+  const heading = !ready ? (fr ? "Préparation…" : "Preparing…")
+    : status === "training" ? (fr ? "Votre pilote apprend" : "Your driver is learning")
+    : status === "evaluating" ? (fr ? "Votre pilote passe les tests" : "Testing your driver")
+    : savedId ? (fr ? "Votre pilote est dans le garage" : "Your driver is in the garage")
+    : tested ? (fr ? "Tests terminés" : "Tests complete")
+    : trained ? (fr ? "Votre pilote peut être testé" : "Your driver is ready to test")
+    : (fr ? "Créez votre premier pilote" : "Create your first driver");
+  return (
+    <div className="training-panel">
+      <div className="eyebrow">{fr ? "VOTRE PILOTE IA" : "YOUR AI DRIVER"}</div>
+      <h1>{fr ? "Apprendre. Tester. Courir." : "Learn. Test. Race."}</h1>
+      <ol className="training-steps" aria-label={fr ? "Étapes" : "Steps"}>
+        {(fr ? ["Entraîner", "Tester", "Sauver", "Courir"] : ["Train", "Test", "Save", "Race"]).map((label,i)=><li key={label} aria-current={step===i+1 ? "step" : undefined}>{i+1}. {label}</li>)}
+      </ol>
+      <div className="training-guide" role="status">
+        <strong>{heading}</strong>
+        <p>{status === "training"
+          ? (fr ? "Il s’exerce en accéléré, d’abord seul puis avec d’autres voitures. Attendez la fin ou arrêtez pour garder ses progrès." : "It practices at accelerated speed, first alone and then in traffic. Wait or stop to keep its progress.")
+          : status === "evaluating"
+          ? (fr ? `${reports.length}/${totalTests} courses testées. Ses connaissances ne changent pas pendant les tests.` : `${reports.length}/${totalTests} races tested. Its weights stay frozen during testing.`)
+          : savedId ? (fr ? "Passez à la course : votre pilote sera déjà sélectionné sur la grille." : "Open racing: your driver will already be selected on the grid.")
+          : tested ? (fr ? `${reports.filter(r=>r.success).length}/${totalTests} courses réussies. Sauvegardez le pilote pour le retrouver en course.` : `${reports.filter(r=>r.success).length}/${totalTests} successful races. Save the driver to race it.`)
+          : trained ? (fr ? "L’entraînement a produit un pilote, pas une garantie de réussite. Testez-le sur les deux circuits." : "Training produced a driver, not a guarantee of success. Test it on both tracks.")
+          : (fr ? "Un clic lance l’apprentissage. Vous pourrez ensuite tester votre pilote et courir contre lui." : "One click starts learning. Then test your driver and race against it.")}</p>
+        {status === "training" && <><progress aria-label={fr ? "Progression de l’entraînement" : "Training progress"} max={budget} value={progress.round}/><span>{Math.round(progress.round/budget*100)} % · {fr ? (progress.traffic ? "Avec du trafic" : "Conduite en solo") : (progress.traffic ? "With traffic" : "Solo driving")}</span></>}
+        {status === "evaluating" && <progress aria-label={fr ? "Progression des tests" : "Evaluation progress"} max={totalTests} value={reports.length}/>}
+      </div>
+      <div className="training-actions">
+        <button className="drive-button" disabled={busy} onClick={()=>savedId ? onRace(savedId) : tested ? save() : trained ? evaluate() : start(true)}>
+          {savedId ? (fr ? "Faire courir mon pilote →" : "Race my driver →") : tested ? (fr ? "Sauvegarder mon pilote" : "Save my driver") : trained ? (fr ? "Tester mon pilote" : "Test my driver") : (fr ? "Entraîner mon pilote" : "Train my driver")}
+        </button>
+        {busy && ready && <button onClick={()=>controller.current?.abort()}>{fr ? "Arrêter" : "Stop"}</button>}
+      </div>
+      <p className="training-preview-note">{fr ? "La voiture à droite est un aperçu de trajectoire par session, pas une course en direct. Pour voir votre pilote conduire en continu, terminez ces étapes puis lancez une course." : "The car is a trajectory snapshot per session, not a live race. To watch continuous driving, complete these steps and launch a race."}</p>
+      {!trained && <label>{fr ? "Comment apprendre ?" : "How should it learn?"}<select value={algorithm} disabled={busy} onChange={e=>setAlgorithm(e.target.value as DriverCheckpoint["algorithm"])}>
+        <option value="imitation-mlp">{fr ? "Imiter un pilote de référence" : "Imitate a reference driver"}</option><option value="dqn">DQN · {fr ? "essais et récompenses" : "trial and reward"}</option><option value="double-dqn">Double DQN · {fr ? "essais et récompenses" : "trial and reward"}</option>
+      </select></label>}
+      {reports.length > 0 && <details className="training-details"><summary>{fr ? "Voir les résultats des courses" : "Race test results"} ({reports.length}/{totalTests})</summary><p>{fr ? "Réussite : 3 tours, sans remise en piste et avec au plus 10 s de pénalité." : "Success: 3 laps, no rescue and at most 10 s penalty."}</p><ul>{reports.map(r=><li key={`${r.test}-${r.traffic}-${r.seed}`}>{r.test ? "Harbour" : "Alpine"} · {r.traffic ? (fr ? "trafic" : "traffic") : "solo"} · {r.seed} : {r.success ? "✓" : "×"} {r.completed ? `${r.finishSeconds?.toFixed(1)} s` : (fr ? "non terminé" : "unfinished")}</li>)}</ul></details>}
+      <details className="training-details"><summary>{fr ? "Réglages et détails techniques" : "Settings and technical details"}</summary>
+        {trained && <label>{fr ? "Méthode d’un nouveau pilote" : "New driver method"}<select value={algorithm} disabled={busy} onChange={e=>setAlgorithm(e.target.value as DriverCheckpoint["algorithm"])}><option value="imitation-mlp">Imitation</option><option value="dqn">DQN</option><option value="double-dqn">Double DQN</option></select></label>}
+        <label>{fr ? "Graine de départ" : "Starting seed"}<input type="number" min="1" max="1000000" value={seed} disabled={busy} onChange={e=>setSeed(Math.max(1,Math.min(1000000,Math.round(Number(e.target.value))||1)))}/></label>
+        <p>{activeAlgorithm} · {progress.samples.toLocaleString(lang)} {fr ? "exemples / transitions" : "samples / transitions"}</p>
+        {progress.loss !== null && <p>{fr ? "Erreur d’imitation" : "Imitation loss"} : {progress.loss.toFixed(4)}</p>}
+        {trained && <div className="training-actions"><button disabled={busy} onClick={()=>start(false)}>{fr ? "Continuer l’entraînement" : "Continue training"}</button><button disabled={busy} onClick={()=>start(true)}>{fr ? "Recommencer avec un nouveau pilote" : "Start a new driver"}</button><button disabled={busy} onClick={save}>{fr ? "Sauvegarder sans attendre les tests" : "Save without waiting for tests"}</button><button disabled={busy} onClick={evaluate}>{fr ? "Refaire les tests" : "Run tests again"}</button></div>}
+        <p><a href={`${import.meta.env.BASE_URL}reports/racing-q-v3/index.html`}>{fr ? "Comparer DQN et Double DQN ↗" : "Compare DQN and Double DQN ↗"}</a></p>
+      </details>
+      <details className="training-details"><summary>{fr ? "Retrouver un pilote sauvegardé" : "Load a saved driver"}</summary><label>{fr ? "Garage local" : "Local garage"}<select value={selected} disabled={busy} onChange={e=>setSelected(e.target.value)}><option value="">{fr ? "Choisir un pilote" : "Choose a driver"}</option>{records.map(r=><option key={r.id} value={r.id}>{r.name}</option>)}</select></label><button disabled={busy || !selected} onClick={load}>{fr ? "Charger ce pilote" : "Load this driver"}</button></details>
+      {saved && <p role="status">{saved}</p>}
+      {error && <p role="alert">{error}</p>}
+    </div>
+  );
+}
diff --git a/packages/demo-car-circuit/src/racing/driving.ts b/packages/demo-car-circuit/src/racing/driving.ts
new file mode 100644
index 0000000..5a70557
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/driving.ts
@@ -0,0 +1,166 @@
+import { CatmullRomCurve3, Vector3 } from "three";
+
+export const DRIVING_CONTRACT = Object.freeze({
+  id: "circuit-racing-v2",
+  dt: 1 / 60,
+  actionCount: 9,
+  maxSpeed: 32,
+  carRadius: 1.2,
+  barrierOffset: 7,
+  barrierThickness: 0.45,
+});
+export interface RoadSample {
+  x: number;
+  z: number;
+  angle: number;
+  distance: number;
+  progress: number;
+}
+export class RacingTrack {
+  readonly width = 9;
+  readonly points: Vector3[];
+  readonly length: number;
+  readonly id: string;
+  constructor(test = false) {
+    this.id = test ? "harbour-test-v1" : "alpine-park-v1";
+    const coords = test
+      ? [
+          [-60, -35],
+          [0, -40],
+          [55, -25],
+          [60, 20],
+          [20, 35],
+          [0, 10],
+          [-40, 40],
+          [-65, 10],
+        ]
+      : [
+          [-30, -32],
+          [20, -32],
+          [55, -20],
+          [60, 10],
+          [30, 20],
+          [12, 40],
+          [-25, 40],
+          [-55, 20],
+          [-62, -10],
+          [-55, -30],
+        ];
+    const curve = new CatmullRomCurve3(
+      coords.map(([x, z]) => new Vector3(x, 0, z)),
+      true,
+      "catmullrom",
+      0.35,
+    );
+    this.points = curve.getSpacedPoints(400);
+    this.length = curve.getLength();
+  }
+  sample(progress: number): RoadSample {
+    const p = (((progress % 1) + 1) % 1) * 400,
+      i = Math.floor(p),
+      a = this.points[i],
+      b = this.points[i + 1],
+      t = p - i;
+    return {
+      x: a.x + (b.x - a.x) * t,
+      z: a.z + (b.z - a.z) * t,
+      angle: Math.atan2(b.z - a.z, b.x - a.x),
+      distance: 0,
+      progress: p / 400,
+    };
+  }
+  nearest(x: number, z: number): RoadSample {
+    let best = Infinity,
+      result = this.sample(0);
+    for (let i = 0; i < 400; i++) {
+      const a = this.points[i],
+        b = this.points[i + 1],
+        dx = b.x - a.x,
+        dz = b.z - a.z;
+      const t = Math.max(
+        0,
+        Math.min(1, ((x - a.x) * dx + (z - a.z) * dz) / (dx * dx + dz * dz)),
+      );
+      const px = a.x + t * dx,
+        pz = a.z + t * dz,
+        d = Math.hypot(x - px, z - pz);
+      if (d < best) {
+        best = d;
+        result = {
+          x: px,
+          z: pz,
+          angle: Math.atan2(dz, dx),
+          distance: d,
+          progress: (i + t) / 400,
+        };
+      }
+    }
+    return result;
+  }
+}
+export interface CarState {
+  x: number;
+  z: number;
+  angle: number;
+  speed: number;
+  steering: number;
+}
+export class DrivingWorld {
+  readonly track: RacingTrack;
+  car: CarState;
+  ticks = 0;
+  constructor(track = new RacingTrack()) {
+    this.track = track;
+    this.car = this.start();
+  }
+  private start(): CarState {
+    const p = this.track.sample(0);
+    return { x: p.x, z: p.z, angle: p.angle, speed: 0, steering: 0 };
+  }
+  reset() {
+    this.car = this.start();
+    this.ticks = 0;
+  }
+  /** Project the collision circle inside the continuous rail and remove outward velocity. */
+  constrainToTrack(): boolean {
+    const c = this.car, p = this.track.nearest(c.x, c.z);
+    const limit = DRIVING_CONTRACT.barrierOffset - DRIVING_CONTRACT.barrierThickness / 2 - DRIVING_CONTRACT.carRadius;
+    if (p.distance <= limit) return false;
+    const nx = (c.x - p.x) / p.distance, nz = (c.z - p.z) / p.distance;
+    c.x = p.x + nx * limit;
+    c.z = p.z + nz * limit;
+    const outward = Math.max(0, Math.cos(c.angle) * nx + Math.sin(c.angle) * nz);
+    c.speed *= Math.max(0, 1 - outward * outward);
+    return true;
+  }
+  step(action: number) {
+    if (!Number.isInteger(action) || action < 0 || action >= 9)
+      throw new Error("Driving action must be an integer in [0,8]");
+    const steer = (action % 3) - 1,
+      throttle = Math.floor(action / 3) - 1,
+      c = this.car,
+      dt = DRIVING_CONTRACT.dt;
+    const offRoad =
+      this.track.nearest(c.x, c.z).distance > this.track.width / 2;
+    c.steering += (steer - c.steering) * Math.min(1, dt * 9);
+    const acceleration = throttle === 1 ? 9 : throttle === -1 ? -20 : 0;
+    c.speed = Math.max(
+      0,
+      Math.min(
+        DRIVING_CONTRACT.maxSpeed,
+        c.speed +
+          (acceleration -
+            0.7 -
+            c.speed * 0.045 -
+            (offRoad ? c.speed * 0.8 : 0)) *
+            dt,
+      ),
+    );
+    c.angle += c.steering * c.speed * 0.12 * dt;
+    c.x += Math.cos(c.angle) * c.speed * dt;
+    c.z += Math.sin(c.angle) * c.speed * dt;
+    const boundaryContact = this.constrainToTrack();
+    this.ticks++;
+    return boundaryContact;
+  }
+}
diff --git a/packages/demo-car-circuit/src/racing/garage.ts b/packages/demo-car-circuit/src/racing/garage.ts
new file mode 100644
index 0000000..7efe417
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/garage.ts
@@ -0,0 +1,50 @@
+import {
+  listDrivers,
+  type SavedDriver,
+  type DriverEvaluation,
+} from "./training";
+import type { DriverCheckpoint } from "./learned-driver";
+export async function loadGarage({
+  storage,
+  base,
+  fetcher = fetch,
+}: {
+  storage: Pick<Storage, "getItem">;
+  base: string;
+  fetcher?: (url: string) => Promise<{ ok: boolean; json(): Promise<unknown> }>;
+}) {
+  const drivers: SavedDriver[] = [],
+    errors: string[] = [];
+  try {
+    drivers.push(...listDrivers(storage));
+  } catch (e) {
+    errors.push(String(e));
+  }
+  const bundled = await Promise.allSettled(
+    [11, 29, 47].map(async (seed) => {
+      const responses = await Promise.all(
+        ["driver", "report"].map((prefix) =>
+          fetcher(`${base}drivers/${prefix}-${seed}.json`),
+        ),
+      );
+      if (responses.some((r) => !r.ok))
+        throw new Error(`Checkpoint bundled-${seed}: asset unavailable`);
+      const [checkpoint, report] = await Promise.all(
+        responses.map((r) => r.json()),
+      );
+      if (!report || !Array.isArray((report as { reports: unknown }).reports))
+        throw new Error(`Checkpoint bundled-${seed}: invalid report`);
+      return {
+        id: `bundled-${seed}`,
+        name: `Imitation ${seed} · 48`,
+        checkpoint: checkpoint as DriverCheckpoint,
+        reports: (report as { reports: DriverEvaluation[] }).reports,
+      };
+    }),
+  );
+  for (const result of bundled) {
+    if (result.status === "fulfilled") drivers.push(result.value);
+    else errors.push(String(result.reason));
+  }
+  return { drivers, errors };
+}
diff --git a/packages/demo-car-circuit/src/racing/learned-driver.ts b/packages/demo-car-circuit/src/racing/learned-driver.ts
new file mode 100644
index 0000000..def94ec
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/learned-driver.ts
@@ -0,0 +1,167 @@
+import * as tf from "@tensorflow/tfjs";
+import { DRIVING_CONTRACT } from "./driving";
+import { OBSERVATION_CONTRACT, OBSERVATION_SIZE } from "./observations";
+import { Q_PROTOCOL } from "./q-protocol";
+import { LEARNING_PROTOCOL } from "./learning-protocol";
+
+export interface DriverCheckpoint {
+  format: "ignition-driver-v1";
+  algorithm: "imitation-mlp" | "dqn" | "double-dqn";
+  driving: string;
+  observation: string;
+  protocol: string;
+  seed: number;
+  updates: number;
+  samples: number;
+  createdAt: string;
+  weights: { shape: number[]; values: number[] }[];
+  configuration?: Record<string, unknown>;
+  evaluation?: unknown;
+}
+const SHAPES = [[20, 32], [32], [32, 32], [32], [32, 9], [9]];
+/** A trained neural network. Inference contains no reference controller or fallback. */
+export class LearnedDriver {
+  private model: tf.Sequential;
+  private optimizer: tf.Optimizer;
+  updates = 0;
+  samples = 0;
+  constructor(readonly seed: number, readonly algorithm: DriverCheckpoint["algorithm"] = "imitation-mlp") {
+    if (!Number.isSafeInteger(seed) || seed < 1 || seed > 1000000)
+      throw new Error("Seed must be an integer from 1 to 1000000");
+    this.model = tf.sequential();
+    this.model.add(
+      tf.layers.dense({
+        inputShape: [OBSERVATION_SIZE],
+        units: 32,
+        activation: algorithm === "imitation-mlp" ? "tanh" : "relu",
+        kernelInitializer: tf.initializers.glorotUniform({ seed }),
+      }),
+    );
+    this.model.add(
+      tf.layers.dense({
+        units: 32,
+        activation: algorithm === "imitation-mlp" ? "tanh" : "relu",
+        kernelInitializer: tf.initializers.glorotUniform({ seed: seed + 1 }),
+      }),
+    );
+    this.model.add(
+      tf.layers.dense({
+        units: 9,
+        activation: algorithm === "imitation-mlp" ? "softmax" : "linear",
+        kernelInitializer: tf.initializers.glorotUniform({ seed: seed + 2 }),
+      }),
+    );
+    this.optimizer = tf.train.adam(algorithm === "imitation-mlp" ? 0.003 : Q_PROTOCOL.learningRate);
+    this.model.compile({
+      optimizer: this.optimizer,
+      loss: algorithm === "imitation-mlp" ? "categoricalCrossentropy" : "meanSquaredError",
+    });
+  }
+  get decisionInterval() { return this.algorithm === "imitation-mlp" ? 1 : Q_PROTOCOL.actionRepeat; }
+  action(observation: number[]): number {
+    if (
+      observation.length !== OBSERVATION_SIZE ||
+      !observation.every(Number.isFinite)
+    )
+      throw new Error("Invalid driving observation");
+    return tf.tidy(() => {
+      const values = (
+        this.model.predict(tf.tensor2d([observation])) as tf.Tensor
+      ).dataSync();
+      return values.indexOf(Math.max(...values));
+    });
+  }
+  async learn(
+    observations: number[][],
+    actions: number[],
+    epochs = 1,
+  ): Promise<number> {
+    if (this.algorithm !== "imitation-mlp") throw new Error("Use Q-learning training for this driver");
+    if (
+      !observations.length ||
+      observations.length !== actions.length ||
+      actions.some((a) => !Number.isInteger(a) || a < 0 || a > 8)
+    )
+      throw new Error("Invalid training samples");
+    const x = tf.tensor2d(observations),
+      y = tf.tidy(() => tf.oneHot(tf.tensor1d(actions, "int32"), 9));
+    try {
+      const history = await this.model.fit(x, y, {
+        epochs,
+        batchSize: 128,
+        shuffle: false,
+        verbose: 0,
+      });
+      this.updates += epochs;
+      this.samples += observations.length;
+      return Number(history.history.loss.at(-1));
+    } finally {
+      x.dispose();
+      y.dispose();
+    }
+  }
+  exportCheckpoint(): DriverCheckpoint {
+    return {
+      configuration: this.algorithm === "imitation-mlp" ? {hiddenLayers:[32,32],learningRate:.003,batchSize:128,epochsPerRound:3,samplesPerRound:4096} : {...Q_PROTOCOL},
+      format: "ignition-driver-v1",
+      algorithm: this.algorithm,
+      driving: DRIVING_CONTRACT.id,
+      observation: OBSERVATION_CONTRACT,
+      protocol: this.algorithm === "imitation-mlp" ? LEARNING_PROTOCOL.id : Q_PROTOCOL.id,
+      seed: this.seed,
+      updates: this.updates,
+      samples: this.samples,
+      createdAt: new Date().toISOString(),
+      weights: this.model
+        .getWeights()
+        .map((t) => ({ shape: t.shape, values: Array.from(t.dataSync()) })),
+    };
+  }
+  static fromCheckpoint(value: unknown): LearnedDriver {
+    const c = value as DriverCheckpoint;
+    if (
+      !c ||
+      c.format !== "ignition-driver-v1" ||
+      !["imitation-mlp", "dqn", "double-dqn"].includes(c.algorithm) ||
+      c.driving !== DRIVING_CONTRACT.id ||
+      c.observation !== OBSERVATION_CONTRACT ||
+      c.protocol !== (c.algorithm === "imitation-mlp" ? LEARNING_PROTOCOL.id : Q_PROTOCOL.id)
+    )
+      throw new Error("Incompatible driver contract");
+    if (
+      !Number.isInteger(c.seed) ||
+      !Number.isInteger(c.updates) ||
+      c.updates < 0 ||
+      !Number.isInteger(c.samples) ||
+      c.samples < 0
+    )
+      throw new Error("Invalid driver metadata");
+    if (
+      !Array.isArray(c.weights) ||
+      c.weights.length !== SHAPES.length ||
+      c.weights.some(
+        (w, i) =>
+          !w ||
+          JSON.stringify(w.shape) !== JSON.stringify(SHAPES[i]) ||
+          !Array.isArray(w.values) ||
+          w.values.length !== SHAPES[i].reduce((a, b) => a * b, 1) ||
+          !w.values.every(Number.isFinite),
+      )
+    )
+      throw new Error("Invalid driver weights");
+    const driver = new LearnedDriver(c.seed, c.algorithm);
+    const weights = c.weights.map((w) => tf.tensor(w.values, w.shape));
+    try {
+      driver.model.setWeights(weights);
+    } finally {
+      tf.dispose(weights);
+    }
+    driver.updates = c.updates;
+    driver.samples = c.samples;
+    return driver;
+  }
+  dispose() {
+    this.model.dispose();
+    this.optimizer.dispose();
+  }
+}
diff --git a/packages/demo-car-circuit/src/racing/learned-race.ts b/packages/demo-car-circuit/src/racing/learned-race.ts
new file mode 100644
index 0000000..81b10a3
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/learned-race.ts
@@ -0,0 +1,71 @@
+import { RaceWorld } from "./race";
+import { LearnedDriver, type DriverCheckpoint } from "./learned-driver";
+import { observeDriver } from "./observations";
+
+export interface RaceEntry {
+  id: string;
+  name: string;
+  model: "race" | "sedan-sports";
+  checkpoint: DriverCheckpoint;
+}
+/** Owns separately loaded, frozen copies; no reference controller or training path. */
+export class LearnedRace {
+  readonly race: RaceWorld;
+  private readonly previousActions: number[] = [];
+  private readonly policies: LearnedDriver[] = [];
+  constructor(
+    readonly entries: readonly RaceEntry[],
+    readonly human = false,
+    test = false,
+    readonly humanModel: RaceEntry["model"] = "race",
+  ) {
+    if (entries.length < (human ? 1 : 2) || entries.length > (human ? 3 : 4))
+      throw new Error("Select 2–4 learned drivers, or 1–3 human opponents");
+    this.race = new RaceWorld({
+      count: entries.length + (human ? 1 : 0),
+      test,
+      ghost: true,
+    });
+    try {
+      for (const entry of entries)
+        this.policies.push(LearnedDriver.fromCheckpoint(entry.checkpoint));
+    } catch (error) {
+      this.dispose();
+      throw error;
+    }
+  }
+  get competitors() {
+    const learned = this.entries.map(({ id, name, model }) => ({
+      id,
+      name,
+      model,
+    }));
+    return this.human
+      ? [{ id: "player", name: "Player", model: this.humanModel }, ...learned]
+      : learned;
+  }
+  step(humanAction = 4) {
+    if (this.race.finished) return;
+    const observations = this.race.drivers.map((d) =>
+      observeDriver(this.race, d.id),
+    );
+    const actions = this.race.drivers.map((_d, i) =>
+      this.human && i === 0
+        ? humanAction
+        : (() => {
+          const policy=this.policies[i-(this.human?1:0)];
+          if(this.race.elapsedTicks % policy.decisionInterval===0 || this.previousActions[i]===undefined)
+            this.previousActions[i]=policy.action(observations[i]);
+          return this.previousActions[i];
+        })(),
+    );
+    this.race.step(actions);
+  }
+  snapshots() {
+    return this.policies.map((p) => p.exportCheckpoint());
+  }
+  dispose() {
+    for (const p of this.policies) p.dispose();
+    this.policies.length = 0;
+  }
+}
diff --git a/packages/demo-car-circuit/src/racing/learning-protocol.ts b/packages/demo-car-circuit/src/racing/learning-protocol.ts
new file mode 100644
index 0000000..45faba3
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/learning-protocol.ts
@@ -0,0 +1,22 @@
+import { DRIVING_CONTRACT } from "./driving";
+import { OBSERVATION_CONTRACT } from "./observations";
+import { RACE_PROTOCOL } from "./race";
+
+/** Fixed before training runs; held-out results must never choose the checkpoint. */
+export const LEARNING_PROTOCOL = Object.freeze({
+  id: "racing-learning-v2",
+  driving: DRIVING_CONTRACT.id,
+  observation: OBSERVATION_CONTRACT,
+  race: RACE_PROTOCOL.id,
+  trainingSeeds: [11, 29, 47],
+  checkpointSelection: "last scheduled update on training circuit only",
+  evaluationSeeds: [101, 307, 509],
+  success: {
+    completedLaps: 3,
+    maxRescues: 0,
+    maxPenalties: 10,
+    minimumCompletionRate: 0.8,
+  },
+  evaluation:
+    "All three training seeds on both circuits, solo and three frozen rule-based traffic drivers. Report every run, including failures.",
+});
diff --git a/packages/demo-car-circuit/src/racing/observations.ts b/packages/demo-car-circuit/src/racing/observations.ts
new file mode 100644
index 0000000..05368e4
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/observations.ts
@@ -0,0 +1,52 @@
+import { RaceWorld } from "./race";
+
+export const OBSERVATION_CONTRACT = "racing-observation-v1";
+export const OBSERVATION_SIZE = 20;
+const clamp = (n: number) => Math.max(-1, Math.min(1, n));
+/** Track-relative sensors, identical in collection, evaluation and races. */
+export function observeDriver(race: RaceWorld, id = 0): number[] {
+  const car = race.drivers[id].world.car;
+  const near = race.track.nearest(car.x, car.z);
+  const heading = Math.atan2(
+    Math.sin(near.angle - car.angle),
+    Math.cos(near.angle - car.angle),
+  );
+  const lateral =
+    (car.x - near.x) * -Math.sin(near.angle) +
+    (car.z - near.z) * Math.cos(near.angle);
+  const result = [
+    car.speed / 32,
+    car.steering,
+    clamp(lateral / (race.track.width / 2)),
+    heading / Math.PI,
+  ];
+  for (const meters of [3 + car.speed * 0.15, 10, 20, 35]) {
+    const target = race.track.sample(
+      near.progress + meters / race.track.length,
+    );
+    const error = Math.atan2(
+      Math.sin(Math.atan2(target.z - car.z, target.x - car.x) - car.angle),
+      Math.cos(Math.atan2(target.z - car.z, target.x - car.x) - car.angle),
+    );
+    result.push(clamp(error / 0.6));
+  }
+  const opponents = race.drivers
+    .filter((d) => d.id !== id)
+    .map((d) => {
+      const dx = d.world.car.x - car.x,
+        dz = d.world.car.z - car.z;
+      return {
+        distance: Math.hypot(dx, dz),
+        values: [
+          clamp((dx * Math.cos(car.angle) + dz * Math.sin(car.angle)) / 30),
+          clamp((-dx * Math.sin(car.angle) + dz * Math.cos(car.angle)) / 10),
+          (d.world.car.speed - car.speed) / 32,
+          1,
+        ],
+      };
+    })
+    .sort((a, b) => a.distance - b.distance);
+  for (let i = 0; i < 3; i++)
+    result.push(...(opponents[i]?.values ?? [0, 0, 0, 0]));
+  return result;
+}
diff --git a/packages/demo-car-circuit/src/racing/q-protocol.ts b/packages/demo-car-circuit/src/racing/q-protocol.ts
new file mode 100644
index 0000000..d9a9342
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/q-protocol.ts
@@ -0,0 +1,23 @@
+/** Fixed before running either algorithm. Last scheduled checkpoint, no evaluation selection. */
+export const Q_PROTOCOL = Object.freeze({
+  id: 'racing-q-learning-v3',
+  seeds: [11, 29, 47, 73, 101],
+  evaluationSeeds: [211, 307, 419, 509, 601],
+  transitions: 20000,
+  actionRepeat: 6,
+  trafficAfterTransitions: 10000,
+  episodeTransitions: 600,
+  termination: 'finish only; off-road recovery follows RaceWorld',
+  truncation: '600 decisions, race time limit, or solo-to-traffic curriculum boundary',
+  trainEvery: 4,
+  hiddenLayers: [32, 32],
+  learningRate: 0.001,
+  batchSize: 32,
+  memorySize: 10000,
+  gamma: 0.99,
+  epsilonDecay: 0.9995,
+  minEpsilon: 0.05,
+  targetUpdateFrequency: 100,
+  evaluation: '5 seeds x 2 tracks x solo/traffic = 20 full races per trained policy',
+  selection: 'last scheduled checkpoint; all failures retained',
+});
diff --git a/packages/demo-car-circuit/src/racing/q-training.ts b/packages/demo-car-circuit/src/racing/q-training.ts
new file mode 100644
index 0000000..87e7c96
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/q-training.ts
@@ -0,0 +1,97 @@
+import * as tf from '@tensorflow/tfjs';
+import { DQNAgent, buildQNetwork } from '@ignitionai/backend-tfjs';
+import { referenceAction } from './reference';
+import { RaceWorld } from './race';
+import { observeDriver } from './observations';
+import { LearnedDriver, type DriverCheckpoint } from './learned-driver';
+import { Q_PROTOCOL } from './q-protocol';
+import { seededRandom, type TrainingProgress } from './training';
+
+/** One RL transition holds an action for six common physics ticks. No teacher labels. */
+export class RacingQEnvironment {
+  race = new RaceWorld({ countdown: 0 });
+  actions = 9;
+  private steps = 0;
+  private lastReward = 0;
+  private stopped = false;
+  private readonly random: () => number;
+  constructor(seed: number, readonly traffic = false) { this.random = seededRandom(seed); this.reset(); }
+  observe() { return observeDriver(this.race); }
+  reset() {
+    this.race = new RaceWorld({countdown:0,count:this.traffic?4:1});
+    const d=this.race.drivers[0], p=this.race.track.sample(this.random());
+    Object.assign(d.world.car,{x:p.x,z:p.z,angle:p.angle+(this.random()-.5)*.1,speed:4+this.random()*8});
+    d.lastProgress=p.progress;d.gates=Math.floor(p.progress*20);d.safeProgress=d.gates/20;
+    this.steps=0;this.lastReward=0;this.stopped=false;
+  }
+  step(action: number) {
+    if(this.done()) return;
+    const d=this.race.drivers[0], before=d.lastProgress, gates=d.gates, penalties=d.penaltySeconds;
+    for(let i=0;i<Q_PROTOCOL.actionRepeat;i++) this.race.step(this.race.drivers.map((driver,index)=>index===0?action:referenceAction(driver.world)));
+    let delta=d.lastProgress-before;
+    if(delta>.5)delta-=1;if(delta<-.5)delta+=1;
+    const offroad=this.race.track.nearest(d.world.car.x,d.world.car.z).distance>this.race.track.width/2;
+    this.lastReward=Math.max(-2,Math.min(2,delta*this.race.track.length)) + (d.gates-gates)*2 - (d.penaltySeconds-penalties) - .01 - (offroad?.2:0);
+    this.steps++;
+    // Recoverable errors keep the same rescue/penalty rules as racing.
+    this.stopped=d.finishSeconds!==null;
+  }
+  reward() { return this.lastReward; }
+  terminated() { return this.stopped; }
+  truncated() { return !this.stopped && (this.steps>=Q_PROTOCOL.episodeTransitions || this.race.finished); }
+  done() { return this.terminated() || this.truncated(); }
+}
+
+export async function trainQDriver(checkpoint: DriverCheckpoint, options: {
+  transitions?: number; signal?: AbortSignal;
+  onWorld?: (race:RaceWorld)=>void; onProgress?: (p:TrainingProgress)=>void;
+} = {}): Promise<DriverCheckpoint> {
+  if(checkpoint.algorithm==='imitation-mlp') throw new Error('Q-learning needs a Q-network checkpoint');
+  const validated=LearnedDriver.fromCheckpoint(checkpoint);validated.dispose();
+  const budget=options.transitions??Q_PROTOCOL.transitions;
+  if(!Number.isInteger(budget)||budget<1)throw new Error('Invalid transition budget');
+  if(!['cpu','tensorflow'].includes(tf.getBackend())) throw new Error('Q training requires the CPU or native TensorFlow backend');
+  let output=checkpoint;
+  const agent=new DQNAgent({
+    inputSize:20,actionSize:9,hiddenLayers:[32,32],seed:checkpoint.seed,
+    doubleQ:checkpoint.algorithm==='double-dqn',backend:'auto',
+    lr:Q_PROTOCOL.learningRate,batchSize:Q_PROTOCOL.batchSize,memorySize:Q_PROTOCOL.memorySize,
+    gamma:Q_PROTOCOL.gamma,epsilonDecay:Q_PROTOCOL.epsilonDecay,minEpsilon:Q_PROTOCOL.minEpsilon,
+    targetUpdateFrequency:Q_PROTOCOL.targetUpdateFrequency,
+    storageProvider:{
+      async load(){
+        const model=buildQNetwork(20,9,[32,32],Q_PROTOCOL.learningRate,checkpoint.seed);
+        const weights=checkpoint.weights.map(w=>tf.tensor(w.values,w.shape));
+        try{model.setWeights(weights);}finally{tf.dispose(weights);}
+        return model;
+      },
+      async save(_id,model){
+        output={...checkpoint,configuration:{...Q_PROTOCOL},createdAt:new Date().toISOString(),
+          samples:checkpoint.samples+collected,updates:checkpoint.updates+Number(agent.getState().trainStepCounter),
+          weights:(model as tf.Sequential).getWeights().map(t=>({shape:t.shape,values:Array.from(t.dataSync())}))};
+        return 'memory';
+      },async list(){return[];},async exists(){return true;},async delete(){},
+    },
+  });
+  let collected=0;
+  let env=new RacingQEnvironment(checkpoint.seed+checkpoint.samples,checkpoint.samples>=Q_PROTOCOL.trafficAfterTransitions);
+  try {
+    await agent.load('weights');
+    for(;collected<budget && !options.signal?.aborted;) {
+      if(!env.traffic && checkpoint.samples+collected>=Q_PROTOCOL.trafficAfterTransitions)
+        env=new RacingQEnvironment(checkpoint.seed+checkpoint.samples+collected,true);
+      const state=env.observe(),action=await agent.getAction(state);
+      env.step(action);
+      agent.remember({state,action,nextState:env.observe(),reward:env.reward(),terminated:env.terminated(),truncated:env.truncated() || (!env.terminated() && checkpoint.samples+collected+1===Q_PROTOCOL.trafficAfterTransitions)});
+      collected++;
+      if(collected%Q_PROTOCOL.trainEvery===0) await agent.train();
+      if(env.done())env.reset();
+      if(collected%250===0) {
+        if(!options.signal?.aborted){options.onWorld?.(env.race);options.onProgress?.({round:collected,loss:null,samples:checkpoint.samples+collected,traffic:env.traffic});}
+        await new Promise(resolve=>setTimeout(resolve,0));
+      }
+    }
+    await agent.save('last');
+    return output;
+  } finally {agent.dispose();}
+}
diff --git a/packages/demo-car-circuit/src/racing/race.ts b/packages/demo-car-circuit/src/racing/race.ts
new file mode 100644
index 0000000..9d35a92
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/race.ts
@@ -0,0 +1,196 @@
+import { DrivingWorld, RacingTrack, DRIVING_CONTRACT } from "./driving";
+
+export const RACE_PROTOCOL = Object.freeze({
+  id: "circuit-race-v2",
+  laps: 3,
+  checkpoints: 20,
+  rescueTicks: 300,
+  immobilizationRadius: 1,
+  rescuePenalty: 5,
+  offroadPenalty: 2,
+  maxTicks: 60 * 300,
+  trainingTrack: "alpine-park-v1",
+  heldOutTrack: "harbour-test-v1",
+  evaluationSeeds: [101, 307, 509],
+});
+export interface RaceDriver {
+  id: number;
+  world: DrivingWorld;
+  laps: number;
+  gates: number;
+  lastProgress: number;
+  safeProgress: number;
+  offroadTicks: number;
+  stationaryTicks: number;
+  motionAnchor: { x: number; z: number };
+  penaltySeconds: number;
+  rescues: number;
+  collisions: number;
+  finishSeconds: number | null;
+}
+/** All decisions are supplied as one batch, before any physics is advanced. */
+export class RaceWorld {
+  readonly track: RacingTrack;
+  readonly drivers: RaceDriver[];
+  readonly countdownTicks: number;
+  readonly ghost: boolean;
+  get rulesId() { return this.ghost ? "circuit-race-v2-ghost-v1" : RACE_PROTOCOL.id; }
+  ticks = 0;
+  elapsedTicks = 0;
+  constructor({
+    count = 1,
+    test = false,
+    countdown = 3,
+    ghost = false,
+  }: { count?: number; test?: boolean; countdown?: number; ghost?: boolean } = {}) {
+    if (!Number.isInteger(count) || count < 1 || count > 4)
+      throw new Error("Race needs 1–4 drivers");
+    this.ghost = ghost;
+    this.track = new RacingTrack(test);
+    this.countdownTicks = Math.round(countdown / DRIVING_CONTRACT.dt);
+    this.drivers = Array.from({ length: count }, (_, id) => {
+      const world = new DrivingWorld(this.track);
+      const p = this.track.sample(0);
+      const side = count === 1 ? 0 : id % 2 ? 1.6 : -1.6;
+      const back = Math.floor(id / 2) * 5;
+      world.car.x += -Math.sin(p.angle) * side - Math.cos(p.angle) * back;
+      world.car.z += Math.cos(p.angle) * side - Math.sin(p.angle) * back;
+      return {
+        id,
+        world,
+        laps: 0,
+        gates: 0,
+        lastProgress: this.track.nearest(world.car.x, world.car.z).progress,
+        safeProgress: 0,
+        offroadTicks: 0,
+        stationaryTicks: 0,
+        motionAnchor: { x: world.car.x, z: world.car.z },
+        penaltySeconds: 0,
+        rescues: 0,
+        collisions: 0,
+        finishSeconds: null,
+      };
+    });
+  }
+  get finished() {
+    return (
+      this.drivers.every((d) => d.finishSeconds !== null) ||
+      this.elapsedTicks >= RACE_PROTOCOL.maxTicks
+    );
+  }
+  get countdown() {
+    return Math.max(
+      0,
+      (this.countdownTicks - this.ticks) * DRIVING_CONTRACT.dt,
+    );
+  }
+  get standings() {
+    return [...this.drivers].sort((a, b) => {
+      if (a.finishSeconds !== null || b.finishSeconds !== null)
+        return (
+          (a.finishSeconds ?? Infinity) - (b.finishSeconds ?? Infinity) ||
+          a.id - b.id
+        );
+      const remaining = (d: RaceDriver) =>
+        (((d.gates + 1) % RACE_PROTOCOL.checkpoints) /
+          RACE_PROTOCOL.checkpoints -
+          d.lastProgress +
+          1) %
+        1;
+      return b.gates - a.gates || remaining(a) - remaining(b) || a.id - b.id;
+    });
+  }
+  private resolveContacts(railContacts: Set<number>) {
+    const pairs = new Set<string>();
+    const diameter = 2 * DRIVING_CONTRACT.carRadius;
+    // Bounded constraint passes keep a rail projection from undoing separation.
+    // Contact metrics and impact damping count once per pair, not per solver pass.
+    for (let iteration = 0; iteration < 16; iteration++) {
+      const moved = new Set<number>();
+      for (let i = 0; i < this.drivers.length; i++) for (let j = i + 1; j < this.drivers.length; j++) {
+        const da = this.drivers[i], db = this.drivers[j];
+        if (da.finishSeconds !== null || db.finishSeconds !== null) continue;
+        const a = da.world.car, b = db.world.car, dx = b.x - a.x, dz = b.z - a.z, dist = Math.hypot(dx, dz);
+        if (dist >= diameter - 1e-4) continue;
+        const nx = dist > 1e-6 ? dx / dist : 1, nz = dist > 1e-6 ? dz / dist : 0, push = (diameter - dist) / 2;
+        a.x -= nx * push; a.z -= nz * push;
+        b.x += nx * push; b.z += nz * push;
+        moved.add(i); moved.add(j);
+        const pair = `${i}:${j}`;
+        if (!pairs.has(pair)) {
+          pairs.add(pair); a.speed *= 0.7; b.speed *= 0.7;
+          da.collisions++; db.collisions++;
+        }
+      }
+      if (moved.size === 0) break;
+      for (const id of moved) if (this.drivers[id].world.constrainToTrack()) railContacts.add(id);
+    }
+  }
+  step(actions: readonly number[]) {
+    if (
+      actions.length !== this.drivers.length ||
+      actions.some((a) => !Number.isInteger(a) || a < 0 || a > 8)
+    )
+      throw new Error("Supply one valid action per driver");
+    if (this.finished) return;
+    this.ticks++;
+    if (this.ticks <= this.countdownTicks) return;
+    this.elapsedTicks++;
+    const railContacts = new Set<number>();
+    for (const d of this.drivers)
+      if (d.finishSeconds === null && d.world.step(actions[d.id])) railContacts.add(d.id);
+    if (!this.ghost) this.resolveContacts(railContacts);
+    for (const id of railContacts) this.drivers[id].collisions++;
+    for (const d of this.drivers) {
+      if (d.finishSeconds !== null) continue;
+      const c = d.world.car,
+        p = this.track.nearest(c.x, c.z);
+      let delta = p.progress - d.lastProgress;
+      if (delta > 0.5) delta -= 1;
+      if (delta < -0.5) delta += 1;
+      const forward = delta > 0 && delta * this.track.length < 2;
+      const next =
+        ((d.gates + 1) % RACE_PROTOCOL.checkpoints) / RACE_PROTOCOL.checkpoints;
+      const distanceToGate = (next - d.lastProgress + 1) % 1;
+      if (
+        p.distance <= this.track.width / 2 &&
+        forward &&
+        distanceToGate > 0 &&
+        distanceToGate <= delta + 1e-8
+      ) {
+        d.gates++;
+        d.safeProgress = next;
+        d.laps = Math.floor(d.gates / RACE_PROTOCOL.checkpoints);
+        if (d.laps >= RACE_PROTOCOL.laps)
+          d.finishSeconds =
+            this.elapsedTicks * DRIVING_CONTRACT.dt + d.penaltySeconds;
+      }
+      d.lastProgress = p.progress;
+      if (d.finishSeconds !== null) continue;
+      if (p.distance > this.track.width / 2 && d.offroadTicks === 0)
+        d.penaltySeconds += RACE_PROTOCOL.offroadPenalty;
+      d.offroadTicks =
+        p.distance > this.track.width / 2 ? d.offroadTicks + 1 : 0;
+      // A moving excursion is recoverable. Rescue only a car remaining within
+      // one metre for five seconds, including a car pinned against traffic/rails.
+      if (Math.hypot(c.x - d.motionAnchor.x, c.z - d.motionAnchor.z) >= RACE_PROTOCOL.immobilizationRadius) {
+        d.motionAnchor = { x: c.x, z: c.z };
+        d.stationaryTicks = 0;
+      } else d.stationaryTicks++;
+      if (d.stationaryTicks >= RACE_PROTOCOL.rescueTicks) {
+        const safe = this.track.sample(d.safeProgress);
+        c.x = safe.x;
+        c.z = safe.z;
+        c.angle = safe.angle;
+        c.speed = 0;
+        c.steering = 0;
+        d.lastProgress = safe.progress;
+        d.offroadTicks = 0;
+        d.stationaryTicks = 0;
+        d.motionAnchor = { x: c.x, z: c.z };
+        d.rescues++;
+        d.penaltySeconds += RACE_PROTOCOL.rescuePenalty;
+      }
+    }
+  }
+}
diff --git a/packages/demo-car-circuit/src/racing/racing.css b/packages/demo-car-circuit/src/racing/racing.css
new file mode 100644
index 0000000..dbf515c
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/racing.css
@@ -0,0 +1,550 @@
+:root {
+  --race-bg: #121619;
+  --race-panel: #1b2024;
+  --race-line: #323a3f;
+  --race-text: #f3f1e8;
+  --race-muted: #9ca6aa;
+  --race-accent: #e6ee58;
+  --race-space: 4px;
+}
+* {
+  box-sizing: border-box;
+}
+.racing-app {
+  background: var(--race-bg);
+  color: var(--race-text);
+  min-height: 100vh;
+  font-family: Arial, Helvetica, sans-serif;
+}
+.racing-app button,
+.racing-app a {
+  font: inherit;
+}
+.racing-app button {
+  cursor: pointer;
+}
+.racing-app button:focus-visible,
+.racing-app a:focus-visible {
+  outline: 3px solid var(--race-accent);
+  outline-offset: 4px;
+}
+.race-header {
+  height: 76px;
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  border-bottom: 1px solid var(--race-line);
+  padding: 0 32px;
+  gap: 24px;
+}
+.race-brand {
+  color: var(--race-text);
+  font-size: 24px;
+  font-weight: 800;
+  text-decoration: none;
+  letter-spacing: -1px;
+}
+.race-brand span {
+  font-size: 12px;
+  color: var(--race-accent);
+  letter-spacing: 2px;
+  margin-left: 16px;
+}
+.race-header nav {
+  display: flex;
+  gap: 32px;
+  align-items: center;
+  height: 100%;
+}
+.race-header nav .active {
+  border-bottom: 3px solid var(--race-accent);
+  height: 100%;
+  display: flex;
+  align-items: center;
+  font-size: 14px;
+}
+.race-header nav button {
+  background: none;
+  border: 0;
+  color: var(--race-muted);
+  font-size: 14px;
+  opacity: 0.55;
+  cursor: not-allowed;
+}
+.language {
+  color: var(--race-text);
+  background: none;
+  border: 1px solid var(--race-line);
+  padding: 8px 12px;
+  font-size: 12px !important;
+}
+.race-layout {
+  display: grid;
+  grid-template-columns: 320px 1fr;
+  min-height: calc(100vh - 76px);
+}
+.race-sidebar {
+  padding: 40px 28px 24px;
+  display: flex;
+  flex-direction: column;
+  border-right: 1px solid var(--race-line);
+}
+.eyebrow {
+  font-size: 9px;
+  font-weight: 700;
+  letter-spacing: 2px;
+  color: var(--race-accent);
+}
+.race-sidebar h1 {
+  font-size: 32px;
+  letter-spacing: -1.5px;
+  line-height: 1.05;
+  margin: 20px 0;
+}
+.race-intro {
+  font-size: 13px;
+  line-height: 1.6;
+  color: var(--race-muted);
+  margin: 0 0 32px;
+}
+.vehicle-label {
+  font-size: 10px;
+  letter-spacing: 2px;
+  color: var(--race-muted);
+  margin-bottom: 12px;
+}
+.vehicle-options {
+  display: grid;
+  gap: 8px;
+}
+.vehicle-options button {
+  display: flex;
+  gap: 16px;
+  align-items: center;
+  padding: 16px 12px;
+  border: 1px solid var(--race-line);
+  background: var(--race-panel);
+  color: var(--race-text);
+  font-size: 13px;
+}
+.vehicle-options button.selected {
+  border-color: var(--race-accent);
+}
+.vehicle-options button span {
+  font-size: 10px;
+  color: var(--race-muted);
+}
+.vehicle-options b {
+  margin-left: auto;
+  color: var(--race-accent);
+}
+.muted {
+  font-size: 10px;
+  color: var(--race-muted);
+  margin: 12px 0 24px;
+}
+.drive-button {
+  display: flex;
+  justify-content: space-between;
+  background: var(--race-accent);
+  color: var(--race-bg);
+  border: 0;
+  padding: 18px 16px;
+  font-weight: 700 !important;
+  font-size: 13px !important;
+}
+.reset-button {
+  border: 0;
+  background: none;
+  color: var(--race-muted);
+  padding: 16px;
+  font-size: 11px !important;
+}
+.race-controls {
+  margin-top: 24px;
+  border-top: 1px solid var(--race-line);
+  padding-top: 24px;
+}
+.race-controls p {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  font-size: 11px;
+  color: var(--race-muted);
+  margin: 8px 0;
+}
+.race-controls kbd {
+  background: var(--race-panel);
+  border: 1px solid var(--race-line);
+  padding: 5px 8px;
+  color: var(--race-text);
+  min-width: 76px;
+  text-align: center;
+  font-size: 10px;
+}
+.local-badge {
+  margin-top: auto;
+  padding-top: 32px;
+  color: var(--race-muted);
+  font-size: 9px;
+  letter-spacing: 1px;
+}
+.local-badge i,
+.session-pill i {
+  display: inline-block;
+  width: 6px;
+  height: 6px;
+  background: var(--race-accent);
+  border-radius: 50%;
+  margin-right: 8px;
+}
+.race-viewport {
+  position: relative;
+  min-width: 0;
+  height: calc(100vh - 76px);
+  min-height: 560px;
+  overflow: hidden;
+}
+.track-label {
+  position: absolute;
+  top: 28px;
+  left: 28px;
+  background: #172127e6;
+  padding: 14px 20px;
+  border-left: 3px solid var(--race-accent);
+}
+.track-label span {
+  display: block;
+  font-size: 14px;
+  font-weight: 800;
+  letter-spacing: 2px;
+}
+.track-label small {
+  display: block;
+  color: #bac4c6;
+  font-size: 10px;
+  margin-top: 6px;
+}
+.session-pill {
+  position: absolute;
+  top: 28px;
+  right: 28px;
+  font-size: 10px;
+  letter-spacing: 1px;
+  background: #172127e6;
+  padding: 14px;
+}
+.driving-hint {
+  position: absolute;
+  bottom: 32px;
+  left: 28px;
+  background: #172127e6;
+  padding: 14px 20px;
+  font-size: 11px;
+  color: #cdd6d7;
+  max-width: 55%;
+}
+.speedometer {
+  position: absolute;
+  bottom: 28px;
+  right: 28px;
+  background: #172127f2;
+  padding: 20px 24px;
+  width: 164px;
+}
+.speedometer > span {
+  font-size: 9px;
+  letter-spacing: 2px;
+  color: var(--race-muted);
+}
+.speedometer strong {
+  display: block;
+  font-size: 56px;
+  line-height: 1.1;
+  letter-spacing: -4px;
+  font-variant-numeric: tabular-nums;
+  font-style: italic;
+}
+.speedometer small {
+  font-size: 10px;
+  color: var(--race-muted);
+}
+.speed-track {
+  height: 3px;
+  background: var(--race-line);
+  margin-top: 16px;
+  overflow: hidden;
+}
+.speed-track i {
+  height: 100%;
+  display: block;
+  background: var(--race-accent);
+}
+.pause-overlay,
+.race-error {
+  position: absolute;
+  inset: 0;
+  background: #111820a8;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  z-index: 4;
+}
+.pause-overlay h2 {
+  font-size: 36px;
+  margin: 0 0 8px;
+  letter-spacing: -1px;
+}
+.pause-overlay p {
+  color: #cad1d1;
+  font-size: 13px;
+}
+.pause-overlay button,
+.race-error button {
+  background: var(--race-accent);
+  border: 0;
+  padding: 16px 28px;
+  margin-top: 16px;
+  color: #121619;
+}
+.race-loading {
+  position: absolute;
+  inset: 0;
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  gap: 20px;
+  background: #1b2024;
+  z-index: 5;
+  font-size: 13px;
+}
+.race-loading progress {
+  accent-color: var(--race-accent);
+  width: 220px;
+}
+@media (max-width: 900px) {
+  .race-layout {
+    grid-template-columns: 250px 1fr;
+  }
+  .race-sidebar {
+    padding: 28px 20px;
+  }
+  .race-header {
+    padding: 0 20px;
+  }
+  .race-header nav {
+    gap: 16px;
+  }
+  .session-pill {
+    display: none;
+  }
+  .race-sidebar h1 {
+    font-size: 28px;
+  }
+  .driving-hint {
+    display: none;
+  }
+}
+@media (max-width: 600px) {
+  .race-layout {
+    display: flex;
+    flex-direction: column-reverse;
+  }
+  .race-viewport {
+    height: 55vh;
+    min-height: 360px;
+  }
+  .race-header {
+    height: 60px;
+    padding: 0 16px;
+  }
+  .race-brand {
+    font-size: 20px;
+  }
+  .race-brand span {
+    font-size: 9px;
+    margin-left: 8px;
+  }
+  .race-header nav {
+    display: none;
+  }
+  .race-sidebar {
+    border: 0;
+    padding: 24px;
+  }
+  .race-sidebar h1 {
+    font-size: 28px;
+  }
+  .track-label {
+    top: 16px;
+    left: 16px;
+  }
+  .speedometer {
+    bottom: 16px;
+    right: 16px;
+    width: 128px;
+    padding: 12px 16px;
+  }
+  .speedometer strong {
+    font-size: 44px;
+  }
+  .race-controls {
+    margin-top: 8px;
+  }
+  .local-badge {
+    padding-top: 16px;
+  }
+}
+
+.race-countdown {
+  position: absolute;
+  inset: 35% 0 auto;
+  text-align: center;
+  color: #edff7a;
+  font-size: 100px;
+  font-weight: 800;
+  pointer-events: none;
+}
+.race-timing {
+  position: absolute;
+  top: 85px;
+  right: 24px;
+  color: white;
+  font-variant-numeric: tabular-nums;
+  background: #17211ddd;
+  padding: 12px 16px;
+  border-radius: 8px;
+}
+
+.race-header nav button:not(:disabled) {
+  opacity: 1;
+  cursor: pointer;
+  color: var(--race-text);
+}
+@media (min-width: 601px) {
+  .race-sidebar {
+    height: calc(100vh - 76px);
+    overflow-y: auto;
+  }
+}
+@media (max-width: 600px) {
+  .race-header {
+    height: auto;
+    min-height: 100px;
+    flex-wrap: wrap;
+    gap: 12px;
+    padding: 12px 16px;
+  }
+  .race-header nav {
+    display: flex;
+    order: 3;
+    width: 100%;
+    height: 30px;
+  }
+}
+
+.pause-overlay ol {
+  list-style: none;
+  width: min(360px, 80%);
+  padding: 0;
+  margin: 20px 0;
+  counter-reset: position;
+}
+.pause-overlay li {
+  counter-increment: position;
+  display: flex;
+  align-items: center;
+  gap: 18px;
+  background: #182329;
+  padding: 12px 18px;
+  margin: 4px 0;
+  font-variant-numeric: tabular-nums;
+  border: 1px solid #394449;
+}
+.pause-overlay li::before {
+  content: counter(position);
+  color: var(--race-accent);
+  font-weight: bold;
+}
+
+.training-panel label {
+  display: grid;
+  gap: 8px;
+  font-size: 12px;
+  margin: 16px 0;
+}
+.training-panel input,
+.training-panel select {
+  width: 100%;
+  background: var(--race-panel);
+  color: var(--race-text);
+  border: 1px solid var(--race-line);
+  padding: 10px;
+}
+.training-actions {
+  display: grid;
+  gap: 8px;
+  margin: 16px 0;
+}
+.training-actions button {
+  border: 1px solid var(--race-line);
+  padding: 12px;
+  background: var(--race-panel);
+  color: var(--race-text);
+  text-align: left;
+}
+.training-actions .drive-button {
+  background: var(--race-accent);
+  color: var(--race-bg);
+}
+.training-actions button:disabled {
+  opacity: 0.4;
+  cursor: not-allowed;
+}
+.training-metrics {
+  border: 1px solid var(--race-line);
+  padding: 16px;
+  font-size: 12px;
+  line-height: 1.6;
+}
+.training-metrics p {
+  margin: 8px 0;
+  color: var(--race-muted);
+}
+.training-metrics ul {
+  padding-left: 16px;
+  font-size: 10px;
+}
+.training-panel [role="alert"] {
+  color: #ffb3a2;
+  font-size: 12px;
+}
+
+.grid-driver { border-top:1px solid var(--race-line); padding:8px 0 16px; }
+.grid-driver small { color:var(--race-muted); font-size:10px; overflow-wrap:anywhere; }
+.spectator-controls { position:absolute; left:28px; top:110px; display:flex; gap:8px; align-items:center; background:#172127ed; color:white; padding:10px; font-size:11px; z-index:3; }
+.spectator-controls select,.spectator-controls button { background:#26343a; color:white; border:1px solid #536065; padding:8px; margin-left:8px; }
+
+.race-map { position:absolute; left:28px; bottom:110px; width:180px; height:124px; background:#152126dc; border:1px solid #556368; border-radius:12px; padding:8px; pointer-events:none; }
+@media(max-width:600px) { .race-map { width:120px;height:84px;left:16px;bottom:16px; } }
+
+.result-driver { flex: 1; min-width: 0; text-align: left; overflow-wrap: anywhere; }
+.result-driver small { display: block; color: var(--race-muted); font-size: 11px; margin-top: 4px; }
+.result-time { white-space: nowrap; }
+
+.training-steps { list-style:none; display:grid; grid-template-columns:repeat(2,1fr); gap:8px; padding:0; font-size:11px; color:var(--race-muted); }
+.training-steps [aria-current="step"] { color:var(--race-accent); font-weight:700; }
+.training-guide { border-left:3px solid var(--race-accent); padding:14px; background:#1b272b; line-height:1.5; font-size:13px; }
+.training-guide strong { font-size:16px; }
+.training-guide progress { display:block; width:100%; margin:12px 0; accent-color:var(--race-accent); }
+.training-preview-note { font-size:12px; color:var(--race-muted); line-height:1.6; }
+.training-details { border-top:1px solid var(--race-line); padding:14px 0; font-size:12px; line-height:1.6; }
+.training-details summary { cursor:pointer; color:var(--race-text); }
+.training-details a { color:var(--race-accent); }
+.training-details button { background:#26343a; border:1px solid #536065; color:white; padding:10px; }
+
+.ghost-notice { color:var(--race-accent); border-left:2px solid currentColor; padding-left:10px; font-size:12px; line-height:1.5; }
+.ghost-race-label { display:block; color:#e6ee58; font-size:10px; letter-spacing:.08em; margin-top:6px; }
+@media(max-width:900px) { .race-timing { top:170px; } }
diff --git a/packages/demo-car-circuit/src/racing/reference.ts b/packages/demo-car-circuit/src/racing/reference.ts
new file mode 100644
index 0000000..30a8f7f
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/reference.ts
@@ -0,0 +1,17 @@
+import { DrivingWorld } from "./driving";
+/** Explicitly labelled rule-based reference; never presented as a learned driver. */
+export function referenceAction(world: DrivingWorld): number {
+  const c = world.car,
+    near = world.track.nearest(c.x, c.z);
+  const target = world.track.sample(
+    near.progress + (3 + c.speed * 0.15) / world.track.length,
+  );
+  const error = Math.atan2(
+    Math.sin(Math.atan2(target.z - c.z, target.x - c.x) - c.angle),
+    Math.cos(Math.atan2(target.z - c.z, target.x - c.x) - c.angle),
+  );
+  const steer = Math.abs(error) < 0.06 ? 0 : Math.sign(error);
+  const desired = Math.abs(error) > 0.5 ? 7 : Math.abs(error) > 0.2 ? 10 : 16;
+  const throttle = c.speed > desired + 1 ? -1 : c.speed < desired ? 1 : 0;
+  return (throttle + 1) * 3 + steer + 1;
+}
diff --git a/packages/demo-car-circuit/src/racing/training.ts b/packages/demo-car-circuit/src/racing/training.ts
new file mode 100644
index 0000000..ca37886
--- /dev/null
+++ b/packages/demo-car-circuit/src/racing/training.ts
@@ -0,0 +1,175 @@
+import { RaceWorld } from "./race";
+import { observeDriver } from "./observations";
+import { referenceAction } from "./reference";
+import { LearnedDriver, type DriverCheckpoint } from "./learned-driver";
+
+export function seededRandom(seed: number) {
+  let state = seed >>> 0;
+  return () => {
+    state = (Math.imul(state, 1664525) + 1013904223) >>> 0;
+    return state / 4294967296;
+  };
+}
+export interface TrainingProgress {
+  round: number;
+  loss: number | null;
+  samples: number;
+  traffic: boolean;
+}
+/** Dataset aggregation: one learner, labels from frozen rule references, no test track. */
+export async function trainDriver(
+  driver: LearnedDriver,
+  options: {
+    rounds: number;
+    seed: number;
+    signal?: AbortSignal;
+    onWorld?: (world: RaceWorld) => void;
+    onProgress?: (p: TrainingProgress) => void;
+  },
+) {
+  const random = seededRandom(options.seed + driver.updates * 997);
+  for (let round = 0; round < options.rounds; round++) {
+    if (options.signal?.aborted) return;
+    const traffic = driver.updates >= 24;
+    const observations: number[][] = [],
+      labels: number[] = [];
+    let race = new RaceWorld({ count: traffic ? 4 : 1, countdown: 0 });
+    // Diverse start states are training-only curriculum, with unchanged race physics.
+    for (let sample = 0; sample < 4096; sample++) {
+      if (sample % 256 === 0) {
+        await new Promise((resolve) => setTimeout(resolve, 0));
+        if (options.signal?.aborted) return;
+      }
+      if (sample % 128 === 0) {
+        race = new RaceWorld({ count: traffic ? 4 : 1, countdown: 0 });
+        const d = race.drivers[0],
+          p = race.track.sample(random());
+        const side = (random() - 0.5) * 4;
+        Object.assign(d.world.car, {
+          x: p.x - Math.sin(p.angle) * side,
+          z: p.z + Math.cos(p.angle) * side,
+          angle: p.angle + (random() - 0.5) * 0.6,
+          speed: random() * 24,
+          steering: (random() - 0.5) * 2,
+        });
+        d.lastProgress = p.progress;
+        d.gates = Math.floor(p.progress * 20);
+        d.safeProgress = d.gates / 20;
+      }
+      const observation = observeDriver(race),
+        label = referenceAction(race.drivers[0].world);
+      observations.push(observation);
+      labels.push(label);
+      const useLearner = driver.updates > 0 && random() < 0.5;
+      const actions = race.drivers.map((d, i) =>
+        i === 0 && useLearner
+          ? driver.action(observation)
+          : referenceAction(d.world),
+      );
+      race.step(actions);
+    }
+    // Deterministic shuffle keeps sequential trajectories from biasing mini-batches.
+    for (let i = observations.length - 1; i > 0; i--) {
+      const j = Math.floor(random() * (i + 1));
+      [observations[i], observations[j]] = [observations[j], observations[i]];
+      [labels[i], labels[j]] = [labels[j], labels[i]];
+    }
+    if (options.signal?.aborted) return;
+    const loss = await driver.learn(observations, labels, 3);
+    if (options.signal?.aborted) return;
+    options.onWorld?.(race);
+    options.onProgress?.({
+      round: round + 1,
+      loss,
+      samples: driver.samples,
+      traffic,
+    });
+    await new Promise((resolve) => setTimeout(resolve, 0));
+  }
+}
+export interface DriverEvaluation {
+  seed: number;
+  test: boolean;
+  traffic: boolean;
+  completed: boolean;
+  success: boolean;
+  laps: number;
+  finishSeconds: number | null;
+  penaltySeconds: number;
+  rescues: number;
+  contacts: number;
+}
+/** Receives a separately loaded network and never updates it. */
+export async function evaluateDriver(
+  driver: LearnedDriver,
+  {
+    seed,
+    test = false,
+    traffic = false,
+    signal,
+  }: { seed: number; test?: boolean; traffic?: boolean; signal?: AbortSignal },
+): Promise<DriverEvaluation> {
+  const race = new RaceWorld({ count: traffic ? 4 : 1, test, countdown: 0 });
+  const random = seededRandom(seed),
+    car = race.drivers[0].world.car;
+  car.angle += (random() - 0.5) * 0.06;
+  let steps = 0, action = 4;
+  while (!race.finished && race.drivers[0].finishSeconds === null) {
+    if (signal?.aborted) throw new Error("Evaluation cancelled");
+    if (steps % driver.decisionInterval === 0) action = driver.action(observeDriver(race));
+    race.step(
+      race.drivers.map((d, i) => (i === 0 ? action : referenceAction(d.world))),
+    );
+    if (++steps % 240 === 0)
+      await new Promise((resolve) => setTimeout(resolve, 0));
+  }
+  const d = race.drivers[0],
+    completed = d.finishSeconds !== null;
+  return {
+    seed,
+    test,
+    traffic,
+    completed,
+    success: completed && d.rescues === 0 && d.penaltySeconds <= 10,
+    laps: d.laps,
+    finishSeconds: d.finishSeconds,
+    penaltySeconds: d.penaltySeconds,
+    rescues: d.rescues,
+    contacts: d.collisions,
+  };
+}
+export interface SavedDriver {
+  id: string;
+  name: string;
+  checkpoint: DriverCheckpoint;
+  reports: DriverEvaluation[];
+}
+const STORE_KEY = "ignition-racing-drivers-v1";
+export function listDrivers(
+  storage: Pick<Storage, "getItem"> = localStorage,
+): SavedDriver[] {
+  const raw = storage.getItem(STORE_KEY);
+  if (!raw) return [];
+  const records: unknown = JSON.parse(raw);
+  if (
+    !Array.isArray(records) ||
+    records.some(
+      (r) =>
+        !r ||
+        typeof r.id !== "string" ||
+        typeof r.name !== "string" ||
+        !r.checkpoint ||
+        !Array.isArray(r.reports),
+    )
+  )
+    throw new Error("Invalid local driver garage");
+  return records as SavedDriver[];
+}
+export function saveDriver(
+  record: SavedDriver,
+  storage: Pick<Storage, "getItem" | "setItem"> = localStorage,
+) {
+  const records = listDrivers(storage).filter((d) => d.id !== record.id);
+  // A single write preserves the previous garage if storage quota is exceeded.
+  storage.setItem(STORE_KEY, JSON.stringify([...records, record]));
+}
diff --git a/packages/demo-car-circuit/src/vite-env.d.ts b/packages/demo-car-circuit/src/vite-env.d.ts
new file mode 100644
index 0000000..11f02fe
--- /dev/null
+++ b/packages/demo-car-circuit/src/vite-env.d.ts
@@ -0,0 +1 @@
+/// <reference types="vite/client" />
diff --git a/packages/demo-car-circuit/test/circuit-env.test.ts b/packages/demo-car-circuit/test/circuit-env.test.ts
index 8d38fd2..29c643b 100644
--- a/packages/demo-car-circuit/test/circuit-env.test.ts
+++ b/packages/demo-car-circuit/test/circuit-env.test.ts
@@ -73,3 +73,23 @@ describe('CircuitEnv', () => {
     expect(env.done()).toBe(true);
   });
 });
+
+it('records a time limit separately from failure and preserves its transition count on reset', () => {
+  const env = new CircuitEnv(10, 4, 2, { maxSteps: 2 });
+  env.step(1);
+  env.step(1);
+  expect(env.terminated()).toBe(false);
+  expect(env.truncated()).toBe(true);
+  env.reset();
+  expect(env.lastEpisode).toEqual({ transitions: 2, completedLaps: 0, lapTimesSeconds: [],
+    simulationSeconds: 0.1, endReason: 'time-limit' });
+  expect(env.stepCount).toBe(0);
+});
+
+it('returns to the configured starting position and direction', () => {
+  const env = new CircuitEnv(6, 6, 2, { startWaypoint: 32 });
+  const initial = env.observe();
+  env.step(0);
+  env.reset();
+  expect(env.observe()).toEqual(initial);
+});
diff --git a/packages/demo-car-circuit/test/evaluation.test.ts b/packages/demo-car-circuit/test/evaluation.test.ts
new file mode 100644
index 0000000..98120e9
--- /dev/null
+++ b/packages/demo-car-circuit/test/evaluation.test.ts
@@ -0,0 +1,53 @@
+import { expect, it, vi } from 'vitest';
+import { evaluateCircuit, CIRCUIT_PROTOCOL } from '../src/evaluation';
+
+it('evaluates a fixed policy reproducibly without training or recording experience', async () => {
+  const policy = { getAction: vi.fn(async (_observation: number[], _greedy?: boolean) => 0), train: vi.fn(), remember: vi.fn() };
+  const options = { policyId: 'left-only-v1', circuit: 'test' as const };
+  const first = await evaluateCircuit(policy, options);
+  const second = await evaluateCircuit(policy, options);
+  expect(second).toEqual(first);
+  expect(first.schemaVersion).toBe(1);
+  expect(first.protocolId).toBe(CIRCUIT_PROTOCOL.id);
+  expect(first.episodes).toHaveLength(3);
+  expect(first.episodes.every(e => e.endReason === 'off-track')).toBe(true);
+  expect(first.totalTransitions).toBeGreaterThan(0);
+  expect(first.offTrackEpisodes).toBe(3);
+  expect(first.completedLaps).toBe(0);
+  expect(policy.train).not.toHaveBeenCalled();
+  expect(policy.remember).not.toHaveBeenCalled();
+  expect(policy.getAction.mock.calls.every(call => call[1] === true)).toBe(true);
+  expect(JSON.parse(JSON.stringify(first))).toEqual(first);
+});
+
+it('declares different training and held-out circuits before evaluation', () => {
+  expect(CIRCUIT_PROTOCOL.circuits.training).not.toEqual(CIRCUIT_PROTOCOL.circuits.test);
+});
+
+it('reports complete laps and simulated lap times for a lane-following reference controller', async () => {
+  const report = await evaluateCircuit({ async getAction(observation) {
+    return observation[1] > 0.008 ? 2 : observation[1] < -0.008 ? 0 : 1;
+  } }, { policyId: 'reference-controller-v1', circuit: 'training' });
+  expect(report.successfulEpisodes).toBe(3);
+  expect(report.completedLaps).toBe(9);
+  for (const episode of report.episodes) {
+    expect(episode.lapTimesSeconds).toHaveLength(3);
+    expect(episode.simulationSeconds).toBeCloseTo(episode.lapTimesSeconds.reduce((a, b) => a + b, 0));
+  }
+});
+
+it('rejects invalid policy actions instead of silently treating them as straight', async () => {
+  await expect(evaluateCircuit({ getAction: async () => 42 }, {
+    policyId: 'invalid', circuit: 'test',
+  })).rejects.toThrow('discrete action');
+});
+
+it('yields to browser tasks while evaluating without changing the report', async () => {
+  let browserTaskRan = false;
+  const task = new Promise<void>(resolve => setTimeout(() => { browserTaskRan = true; resolve(); }, 0));
+  await evaluateCircuit({ async getAction(observation) {
+    return observation[1] > 0.008 ? 2 : observation[1] < -0.008 ? 0 : 1;
+  } }, { policyId: 'reference-controller-v1', circuit: 'training' });
+  expect(browserTaskRan).toBe(true);
+  await task;
+});
diff --git a/packages/demo-car-circuit/test/learned-race.test.ts b/packages/demo-car-circuit/test/learned-race.test.ts
new file mode 100644
index 0000000..10b701d
--- /dev/null
+++ b/packages/demo-car-circuit/test/learned-race.test.ts
@@ -0,0 +1,60 @@
+import { expect, it } from "vitest";
+import { readFileSync } from "node:fs";
+import { LearnedRace, type RaceEntry } from "../src/racing/learned-race";
+const entries: RaceEntry[] = [11, 29].map((seed) => ({
+  id: `checkpoint-${seed}`,
+  name: `Driver ${seed}`,
+  model: "race",
+  checkpoint: JSON.parse(
+    readFileSync(
+      new URL(`../src/public/drivers/driver-${seed}.json`, import.meta.url),
+      "utf8",
+    ),
+  ),
+}));
+it("runs separately loaded learned policies to a result without changing their weights", () => {
+  const session = new LearnedRace(entries);
+  const before = session.snapshots().map((p) => p.weights);
+  while (!session.race.finished) session.step();
+  expect(session.race.drivers.every((d) => d.laps === 3)).toBe(true);
+  expect(session.snapshots().map((p) => p.weights)).toEqual(before);
+  expect(session.entries.map((e) => e.id)).toEqual([
+    "checkpoint-11",
+    "checkpoint-29",
+  ]);
+  session.dispose();
+}, 30000);
+it("rejects a missing or incompatible opponent instead of substituting reference rules", () => {
+  expect(() => new LearnedRace([])).toThrow(/Select/);
+  expect(
+    () =>
+      new LearnedRace([
+        entries[0],
+        {
+          ...entries[1],
+          checkpoint: { ...entries[1].checkpoint, driving: "old" },
+        },
+      ]),
+  ).toThrow(/contract/);
+});
+
+it("applies human commands on the same clock while the saved opponent uses its own policy", () => {
+  const session = new LearnedRace([entries[0]], true);
+  const before = session.snapshots().map((p) => p.weights);
+  for (let i = 0; i < 240; i++) session.step(7);
+  expect(session.race.drivers[0].world.car.speed).toBeGreaterThan(5);
+  expect(session.race.drivers[0].world.ticks).toBe(
+    session.race.drivers[1].world.ticks,
+  );
+  const speed = session.race.drivers[0].world.car.speed;
+  for (let i = 0; i < 20; i++) session.step(1);
+  expect(session.race.drivers[0].world.car.speed).toBeLessThan(speed);
+  expect(session.snapshots().map((p) => p.weights)).toEqual(before);
+  session.dispose();
+});
+
+
+it.each([false,true])('uses ghost mode for learned races, including human=%s', human => {
+  const session=new LearnedRace(human ? [entries[0]] : entries,human);
+  try {expect(session.race.ghost).toBe(true);} finally {session.dispose();}
+});
diff --git a/packages/demo-car-circuit/test/q-training.test.ts b/packages/demo-car-circuit/test/q-training.test.ts
new file mode 100644
index 0000000..d2e01ed
--- /dev/null
+++ b/packages/demo-car-circuit/test/q-training.test.ts
@@ -0,0 +1,43 @@
+import { expect, it } from 'vitest';
+import * as tf from '@tensorflow/tfjs';
+import { LearnedDriver } from '../src/racing/learned-driver';
+import { RacingQEnvironment, trainQDriver } from '../src/racing/q-training';
+import { LearnedRace } from '../src/racing/learned-race';
+
+it.each(['dqn','double-dqn'] as const)('trains %s and reloads its actual weights into frozen racing opponents', async algorithm=>{
+  await tf.setBackend('cpu');
+  const driver=new LearnedDriver(11,algorithm), before=driver.exportCheckpoint();driver.dispose();
+  const trained=await trainQDriver(before,{transitions:64});
+  expect(trained.algorithm).toBe(algorithm);expect(trained.samples).toBe(64);expect(trained.updates).toBeGreaterThan(0);
+  expect(trained.weights).not.toEqual(before.weights);
+  const race=new LearnedRace([{id:'one',name:'one',model:'race',checkpoint:trained},{id:'two',name:'two',model:'sedan-sports',checkpoint:trained}]);
+  try {
+    const snapshots=race.snapshots();for(let i=0;i<200;i++)race.step();
+    expect(race.snapshots().map(p=>p.weights)).toEqual(snapshots.map(p=>p.weights));
+    expect(race.snapshots().map(p=>p.algorithm)).toEqual([algorithm,algorithm]);
+  }finally{race.dispose();}
+});
+it('uses the shared nine actions and deterministic training starts',()=>{
+  const a=new RacingQEnvironment(29),b=new RacingQEnvironment(29);
+  expect(a.actions).toBe(9);expect(a.observe()).toHaveLength(20);
+  for(let i=0;i<50;i++){a.step(7);b.step(7);expect(a.observe()).toEqual(b.observe());expect(a.reward()).toBe(b.reward());if(a.done()){a.reset();b.reset();}}
+  expect(()=>a.step(9)).toThrow();
+});
+it('keeps a recoverable off-road excursion alive until the common race rescue',()=>{
+  const env=new RacingQEnvironment(11),d=env.race.drivers[0];
+  d.world.car.x=1000;d.world.car.z=1000;d.world.car.speed=0;
+  env.step(4);
+  expect(env.terminated()).toBe(false);expect(env.truncated()).toBe(false);
+  for(let i=1;i<51;i++)env.step(4);
+  expect(d.rescues).toBe(1);expect(d.penaltySeconds).toBeGreaterThanOrEqual(7);
+  expect(env.done()).toBe(false);
+});
+it('collects the traffic phase with three frozen references and the same physics clock',()=>{
+  const env=new RacingQEnvironment(11,true);
+  expect(env.race.drivers).toHaveLength(4);
+  const before=env.race.drivers.slice(1).map(d=>[d.world.car.x,d.world.car.z]);
+  env.step(7);
+  expect(env.race.elapsedTicks).toBe(6);
+  expect(env.observe()).toHaveLength(20);
+  expect(env.race.drivers.slice(1).map(d=>[d.world.car.x,d.world.car.z])).not.toEqual(before);
+});
diff --git a/packages/demo-car-circuit/test/racing-driving.test.ts b/packages/demo-car-circuit/test/racing-driving.test.ts
new file mode 100644
index 0000000..64527e2
--- /dev/null
+++ b/packages/demo-car-circuit/test/racing-driving.test.ts
@@ -0,0 +1,42 @@
+import { it, expect } from "vitest";
+import { DrivingWorld, DRIVING_CONTRACT } from "../src/racing/driving";
+
+it("accelerates, brakes and advances on the public fixed-step contract", () => {
+  const world = new DrivingWorld();
+  const start = { ...world.car };
+  for (let i = 0; i < 60; i++) world.step(7);
+  expect(world.car.speed).toBeGreaterThan(5);
+  expect(
+    Math.hypot(world.car.x - start.x, world.car.z - start.z),
+  ).toBeGreaterThan(2);
+  const speed = world.car.speed;
+  for (let i = 0; i < 20; i++) world.step(1);
+  expect(world.car.speed).toBeLessThan(speed);
+  expect(DRIVING_CONTRACT.actionCount).toBe(9);
+});
+it("repeats the same trajectory with the same commands", () => {
+  const a = new DrivingWorld(),
+    b = new DrivingWorld();
+  for (let i = 0; i < 100; i++) {
+    a.step(i < 50 ? 7 : 8);
+    b.step(i < 50 ? 7 : 8);
+  }
+  expect(a.car).toEqual(b.car);
+  expect(() => a.step(42)).toThrow();
+});
+
+it('can recover from a stationary off-road position', () => {
+  const world = new DrivingWorld();
+  world.car.z -= 12;
+  for (let i=0;i<60;i++) world.step(7);
+  expect(world.car.speed).toBeGreaterThan(1);
+});
+
+
+it.each([-1, 1])('contains a car hitting boundary side %s and dissipates impact speed', side => {
+  const world = new DrivingWorld(), p = world.track.sample(0.1);
+  Object.assign(world.car, {x:p.x-Math.sin(p.angle)*5.5*side, z:p.z+Math.cos(p.angle)*5.5*side, angle:p.angle+side*Math.PI/2, speed:20});
+  world.step(7);
+  expect(world.track.nearest(world.car.x,world.car.z).distance).toBeLessThanOrEqual(5.576);
+  expect(world.car.speed).toBeLessThan(2);
+});
diff --git a/packages/demo-car-circuit/test/racing-garage.test.ts b/packages/demo-car-circuit/test/racing-garage.test.ts
new file mode 100644
index 0000000..34f78c6
--- /dev/null
+++ b/packages/demo-car-circuit/test/racing-garage.test.ts
@@ -0,0 +1,31 @@
+import { expect, it } from "vitest";
+import { loadGarage } from "../src/racing/garage";
+import { saveDriver } from "../src/racing/training";
+import { LearnedDriver } from "../src/racing/learned-driver";
+it("keeps a saved local driver available when bundled assets fail", async () => {
+  let data: string | null = null;
+  const storage = {
+    getItem: () => data,
+    setItem: (_key: string, value: string) => {
+      data = value;
+    },
+  };
+  const driver = new LearnedDriver(11);
+  saveDriver(
+    {
+      id: "local-one",
+      name: "Local one",
+      checkpoint: driver.exportCheckpoint(),
+      reports: [],
+    },
+    storage,
+  );
+  const garage = await loadGarage({
+    storage,
+    base: "/",
+    fetcher: async () => ({ ok: false, json: async () => null }),
+  });
+  expect(garage.drivers.map((d) => d.id)).toEqual(["local-one"]);
+  expect(garage.errors).toHaveLength(3);
+  driver.dispose();
+});
diff --git a/packages/demo-car-circuit/test/racing-learning.test.ts b/packages/demo-car-circuit/test/racing-learning.test.ts
new file mode 100644
index 0000000..4cb1c1d
--- /dev/null
+++ b/packages/demo-car-circuit/test/racing-learning.test.ts
@@ -0,0 +1,139 @@
+import { expect, it } from "vitest";
+import { RaceWorld } from "../src/racing/race";
+import { observeDriver, OBSERVATION_SIZE } from "../src/racing/observations";
+import { LearnedDriver } from "../src/racing/learned-driver";
+
+it("uses the same finite observation schema with solo and traffic worlds", () => {
+  for (const count of [1, 4]) {
+    const race = new RaceWorld({ count });
+    expect(observeDriver(race)).toHaveLength(OBSERVATION_SIZE);
+    expect(observeDriver(race).every(Number.isFinite)).toBe(true);
+  }
+});
+it("learns, serializes actual weights, and reloads identical frozen decisions", async () => {
+  const driver = new LearnedDriver(11);
+  const observation = observeDriver(new RaceWorld());
+  const before = driver.exportCheckpoint();
+  await driver.learn(
+    Array.from({ length: 32 }, () => observation),
+    Array(32).fill(7),
+    15,
+  );
+  const saved = driver.exportCheckpoint();
+  expect(saved.weights).not.toEqual(before.weights);
+  const loaded = LearnedDriver.fromCheckpoint(
+    JSON.parse(JSON.stringify(saved)),
+  );
+  expect(loaded.action(observation)).toBe(driver.action(observation));
+  expect(loaded.exportCheckpoint().weights).toEqual(saved.weights);
+  expect(saved.updates).toBeGreaterThan(0);
+  driver.dispose();
+  loaded.dispose();
+});
+it("rejects old contracts and malformed weight payloads before loading", () => {
+  const driver = new LearnedDriver(11),
+    saved = driver.exportCheckpoint();
+  expect(() =>
+    LearnedDriver.fromCheckpoint({ ...saved, driving: "constant-speed-v1" }),
+  ).toThrow(/contract/i);
+  expect(() => LearnedDriver.fromCheckpoint({ ...saved, weights: [] })).toThrow(
+    /weights/i,
+  );
+  driver.dispose();
+});
+
+import { readFileSync } from "node:fs";
+import {
+  evaluateDriver,
+  listDrivers,
+  saveDriver,
+} from "../src/racing/training";
+it("completes a race using persisted learned weights without changing them", async () => {
+  const checkpoint = JSON.parse(
+    readFileSync(
+      new URL("../src/public/drivers/driver-11.json", import.meta.url),
+      "utf8",
+    ),
+  );
+  const driver = LearnedDriver.fromCheckpoint(checkpoint);
+  const before = driver.exportCheckpoint().weights;
+  const result = await evaluateDriver(driver, { seed: 101 });
+  expect(result.laps).toBe(3);
+  expect(result.completed).toBe(true);
+  expect(driver.exportCheckpoint().weights).toEqual(before);
+  driver.dispose();
+}, 30000);
+it("round-trips local driver metadata and preserves the garage on a quota failure", () => {
+  let value: string | null = null;
+  const storage = {
+    getItem: () => value,
+    setItem: (_key: string, next: string) => {
+      value = next;
+    },
+  };
+  const driver = new LearnedDriver(11);
+  const saved = {
+    id: "my-driver",
+    name: "My driver",
+    checkpoint: driver.exportCheckpoint(),
+    reports: [],
+  };
+  saveDriver(saved, storage);
+  expect(listDrivers(storage)).toEqual([saved]);
+  const before = value;
+  expect(() =>
+    saveDriver(
+      { ...saved, id: "second" },
+      {
+        ...storage,
+        setItem: () => {
+          throw new Error("quota");
+        },
+      },
+    ),
+  ).toThrow("quota");
+  expect(value).toBe(before);
+  expect(() => listDrivers({ getItem: () => "[null]" })).toThrow(/garage/);
+  driver.dispose();
+});
+
+import { vi } from "vitest";
+import { trainDriver } from "../src/racing/training";
+it("does not publish an obsolete training world when cancelled during the update", async () => {
+  const driver = new LearnedDriver(11),
+    controller = new AbortController();
+  const onWorld = vi.fn(),
+    onProgress = vi.fn();
+  vi.spyOn(driver, "learn").mockImplementation(async () => {
+    controller.abort();
+    return 0;
+  });
+  await trainDriver(driver, {
+    seed: 11,
+    rounds: 1,
+    signal: controller.signal,
+    onWorld,
+    onProgress,
+  });
+  expect(onWorld).not.toHaveBeenCalled();
+  expect(onProgress).not.toHaveBeenCalled();
+  driver.dispose();
+});
+it("rejects noninteger seeds before creating an unloadable checkpoint", () => {
+  expect(() => new LearnedDriver(11.5)).toThrow(/Seed/);
+});
+
+import * as tf from '@tensorflow/tfjs';
+it('keeps tensor usage stable across checkpoint exports and frozen inference',()=> {
+  const driver=new LearnedDriver(11),observation=observeDriver(new RaceWorld());
+  driver.action(observation);
+  const before=tf.memory().numTensors;
+  for(let i=0;i<50;i++) {driver.exportCheckpoint();driver.action(observation);}
+  expect(tf.memory().numTensors).toBe(before);
+  driver.dispose();
+});
+
+it('rejects archived race-V1 checkpoints after the boundary/recovery contract change', () => {
+  const old = JSON.parse(readFileSync(new URL('../src/public/reports/imitation-v1/driver-11.json',import.meta.url),'utf8'));
+  expect(() => LearnedDriver.fromCheckpoint(old)).toThrow(/contract/i);
+});
diff --git a/packages/demo-car-circuit/test/racing-race.test.ts b/packages/demo-car-circuit/test/racing-race.test.ts
new file mode 100644
index 0000000..f3036bc
--- /dev/null
+++ b/packages/demo-car-circuit/test/racing-race.test.ts
@@ -0,0 +1,142 @@
+import { describe, expect, it } from "vitest";
+import { RaceWorld } from "../src/racing/race";
+
+describe("shared racing rules", () => {
+  it("holds every car for the countdown then advances a common tick", () => {
+    const race = new RaceWorld({ count: 4 });
+    const initial = race.drivers.map((d) => ({ ...d.world.car }));
+    for (let i = 0; i < 180; i++) race.step([7, 7, 7, 7]);
+    expect(race.drivers.map((d) => d.world.car)).toEqual(initial);
+    race.step([7, 7, 7, 7]);
+    expect(race.drivers.every((d) => d.world.ticks === 1)).toBe(true);
+  });
+  it("rejects an invalid batch before any car moves", () => {
+    const race = new RaceWorld({ count: 2, countdown: 0 });
+    expect(() => race.step([7, 9])).toThrow();
+    expect(race.ticks).toBe(0);
+    expect(race.drivers[0].world.ticks).toBe(0);
+  });
+  it("cannot count teleports or backwards start-line crossings as laps", () => {
+    const race = new RaceWorld({ count: 1, countdown: 0 });
+    const d = race.drivers[0];
+    for (const progress of [0.3, 0.6, 0.9, 0.01, 0.99, 0.01]) {
+      Object.assign(d.world.car, race.track.sample(progress));
+      race.step([4]);
+    }
+    expect(d.laps).toBe(0);
+  });
+  it("separates colliding cars and rescues stranded cars with an explicit penalty", () => {
+    const race = new RaceWorld({ count: 2, countdown: 0 });
+    Object.assign(race.drivers[1].world.car, race.drivers[0].world.car);
+    race.step([4, 4]);
+    const [a, b] = race.drivers.map((d) => d.world.car);
+    expect(Math.hypot(a.x - b.x, a.z - b.z)).toBeGreaterThanOrEqual(2.39);
+    a.x = 1000;
+    for (let i = 0; i < 301; i++) race.step([4, 4]);
+    expect(race.drivers[0].rescues).toBe(1);
+    expect(race.drivers[0].penaltySeconds).toBe(7);
+    expect(race.track.nearest(a.x, a.z).distance).toBeLessThan(1);
+  });
+});
+
+import { referenceAction } from "../src/racing/reference";
+it.each([false, true])(
+  "completes three ordered laps on track test=%s with a labelled rule-based reference",
+  (test) => {
+    const race = new RaceWorld({ count: 1, countdown: 0, test });
+    while (!race.finished)
+      race.step(race.drivers.map((d) => referenceAction(d.world)));
+    expect(race.drivers[0].laps).toBe(3);
+    expect(race.drivers[0].finishSeconds).not.toBeNull();
+    expect(race.standings[0].id).toBe(0);
+  },
+);
+
+it("ranks simultaneous identical finishes deterministically and freezes completed results", () => {
+  const race = new RaceWorld({ count: 2, countdown: 0 });
+  for (const d of race.drivers) d.finishSeconds = 100;
+  expect(race.standings.map((d) => d.id)).toEqual([0, 1]);
+  race.step([7, 7]);
+  expect(race.ticks).toBe(0);
+});
+it("runs four reference drivers on one common clock to completion", () => {
+  const race = new RaceWorld({ count: 4, countdown: 0 });
+  while (!race.finished)
+    race.step(race.drivers.map((d) => referenceAction(d.world)));
+  expect(race.drivers.every((d) => d.laps === 3)).toBe(true);
+  expect(race.standings.map((d) => d.finishSeconds)).toEqual(
+    race.standings.map((d) => d.finishSeconds).sort((a, b) => a! - b!),
+  );
+});
+
+it("ranks the front grid ahead of the rear row across the start-line wrap", () => {
+  const race = new RaceWorld({ count: 4 });
+  expect(
+    race.standings
+      .slice(0, 2)
+      .map((d) => d.id)
+      .sort(),
+  ).toEqual([0, 1]);
+});
+
+
+it('rescues prolonged immobilization on the road, but does not rescue a moving off-road car', () => {
+  const stopped = new RaceWorld({countdown:0});
+  for(let i=0;i<300;i++) stopped.step([4]);
+  expect(stopped.drivers[0].rescues).toBe(1);
+  expect(stopped.drivers[0].penaltySeconds).toBe(5);
+
+  const moving = new RaceWorld({countdown:0}), d=moving.drivers[0];
+  for(let i=0;i<360;i++) {
+    const p=moving.track.sample(i/moving.track.length*0.08);
+    Object.assign(d.world.car,{x:p.x-Math.sin(p.angle)*5,z:p.z+Math.cos(p.angle)*5,angle:p.angle,speed:0});
+    moving.step([4]);
+  }
+  expect(d.rescues).toBe(0);
+  expect(d.penaltySeconds).toBe(2);
+});
+
+
+it('freezes the finish before a same-tick immobilization rescue can alter the result', () => {
+  const race=new RaceWorld({countdown:0}), d=race.drivers[0];
+  const p=race.track.sample(1-0.000001);
+  Object.assign(d.world.car,{x:p.x,z:p.z,angle:p.angle,speed:0.1});
+  d.lastProgress=p.progress;d.gates=59;d.laps=2;d.stationaryTicks=299;
+  d.motionAnchor={x:p.x,z:p.z};
+  race.step([4]);
+  expect(d.laps).toBe(3);
+  expect(d.finishSeconds).not.toBeNull();
+  expect(d.rescues).toBe(0);
+  expect(d.penaltySeconds).toBe(0);
+});
+
+
+it('keeps colliding cars separated when one is pinned against a rail', () => {
+  const race = new RaceWorld({count:2,countdown:0}), p=race.track.sample(0.1);
+  race.drivers.forEach((d,i) => Object.assign(d.world.car,{x:p.x-Math.sin(p.angle)*(i?4:5.57),z:p.z+Math.cos(p.angle)*(i?4:5.57),angle:p.angle,speed:0}));
+  race.step([4,4]);
+  const [a,b]=race.drivers.map(d=>d.world.car);
+  expect(Math.hypot(a.x-b.x,a.z-b.z)).toBeGreaterThanOrEqual(2.39);
+  for(const d of race.drivers) expect(race.track.nearest(d.world.car.x,d.world.car.z).distance).toBeLessThanOrEqual(5.576);
+  expect(race.drivers.map(d=>d.collisions)).toEqual([2,1]);
+});
+
+
+it('lets ghost cars overlap without pushing or slowing each other', () => {
+  const race = new RaceWorld({count:2,countdown:0,ghost:true});
+  const solo = new RaceWorld({countdown:0});
+  for(const d of race.drivers) Object.assign(d.world.car,solo.drivers[0].world.car);
+  for(let i=0;i<90;i++) {race.step([7,7]);solo.step([7]);}
+  for(const d of race.drivers) {
+    expect(d.world.car).toEqual(solo.drivers[0].world.car);
+    expect(d.collisions).toBe(0);
+  }
+});
+it('keeps rail collisions active in ghost races', () => {
+  const race = new RaceWorld({countdown:0,ghost:true}), d=race.drivers[0], p=race.track.sample(.1);
+  Object.assign(d.world.car,{x:p.x-Math.sin(p.angle)*5.5,z:p.z+Math.cos(p.angle)*5.5,angle:p.angle+Math.PI/2,speed:20});
+  race.step([7]);
+  expect(race.track.nearest(d.world.car.x,d.world.car.z).distance).toBeLessThanOrEqual(5.576);
+  expect(d.world.car.speed).toBeLessThan(2);
+  expect(d.collisions).toBe(1);
+});
diff --git a/packages/demo-car-circuit/test/track.test.ts b/packages/demo-car-circuit/test/track.test.ts
index ab8fd45..add2bd9 100644
--- a/packages/demo-car-circuit/test/track.test.ts
+++ b/packages/demo-car-circuit/test/track.test.ts
@@ -54,3 +54,8 @@ describe('OvalTrack', () => {
     expect(track.totalLength).toBeLessThan(60);
   });
 });
+
+it('rejects points far beyond the end of a straight even when collinear', () => {
+  const track = new OvalTrack();
+  expect(track.isOnTrack(100, 4)).toBe(false);
+});
diff --git a/packages/web/components/demo-catalog.tsx b/packages/web/components/demo-catalog.tsx
new file mode 100644
index 0000000..3a5263f
--- /dev/null
+++ b/packages/web/components/demo-catalog.tsx
@@ -0,0 +1,10 @@
+import demos from '../data/demos.json'
+
+export default function DemoCatalog() {
+  return <>
+    <p>{demos.length} browser demos share this catalogue with the homepage and static build.</p>
+    <table style={{ width: '100%', borderCollapse: 'collapse', marginTop: 24 }}><thead><tr><th style={{ textAlign: 'left', padding: '12px 16px', borderBottom: '1px solid #64748b' }}>Demo</th><th style={{ textAlign: 'left', padding: '12px 16px', borderBottom: '1px solid #64748b' }}>Methods</th><th style={{ textAlign: 'left', padding: '12px 16px', borderBottom: '1px solid #64748b' }}>Run locally</th></tr></thead><tbody>
+      {demos.map(demo=><tr key={demo.slug}><td style={{ verticalAlign: 'top', padding: '16px', borderBottom: '1px solid #64748b66' }}><a style={{ color: '#60a5fa', fontWeight: 600 }} href={`/demos/${demo.slug}/`}>{demo.title}</a><p style={{ fontSize: 14, lineHeight: 1.6, marginTop: 8 }}>{demo.description}</p></td><td style={{ verticalAlign: 'top', padding: '16px', borderBottom: '1px solid #64748b66' }}>{demo.algos}</td><td style={{ verticalAlign: 'top', padding: '16px', borderBottom: '1px solid #64748b66' }}><code style={{ fontSize: 13, overflowWrap: 'anywhere' }}>pnpm --filter {demo.pkg} dev</code></td></tr>)}
+    </tbody></table>
+  </>
+}
diff --git a/packages/web/components/demos.tsx b/packages/web/components/demos.tsx
index 88a69a0..a99fc94 100644
--- a/packages/web/components/demos.tsx
+++ b/packages/web/components/demos.tsx
@@ -1,64 +1,4 @@
-import { MobileInferenceBadge } from './mobile-inference-badge'
-const DEMOS = [
-  {
-    slug: 'maze',
-    title: 'Maze',
-    description: 'A low-poly humanoid trains in a Rapier 3D arena with prefab walls, sensor rays, doors, and moving hazards.',
-    tech: 'R3F · Rapier',
-    algos: 'Q-Table · DQN · PPO',
-    accent: '#22c55e',
-    featured: true,
-  },
-  {
-    slug: 'cartpole',
-    title: 'CartPole',
-    description: 'Classic pole-balancing benchmark with Euler physics. Converges fast.',
-    tech: '2D Canvas',
-    algos: 'DQN · PPO',
-    accent: '#3b82f6',
-  },
-  {
-    slug: 'mountaincar',
-    title: 'MountainCar',
-    description: 'Sparse reward challenge. Agent discovers the counterintuitive momentum strategy.',
-    tech: '2D Canvas',
-    algos: 'DQN · PPO',
-    accent: '#f59e0b',
-  },
-  {
-    slug: 'cartpole-3d',
-    title: 'CartPole 3D',
-    description: 'Classic CartPole rendered in 3D with React Three Fiber. Metallic materials and shadows.',
-    tech: 'R3F',
-    algos: 'DQN · PPO',
-    accent: '#6366f1',
-  },
-  {
-    slug: 'car-circuit',
-    title: 'Car Circuit',
-    description: 'A 3D car learns to drive an oval circuit. Chase cam, minimap, trail, 1×–50× speed.',
-    tech: 'R3F',
-    algos: 'DQN · PPO',
-    accent: '#ec4899',
-  },
-  {
-    slug: 'drone-navigation',
-    title: 'Drone Navigation',
-    description: 'A quadcopter learns to fly to moving target points. Rigid-body physics, 8 thrust combos, real gravity and torque. The hero demo.',
-    tech: 'R3F · Physics',
-    algos: 'DQN',
-    accent: '#a855f7',
-  },
-  {
-    slug: 'maze-3d',
-    title: 'Maze 3D',
-    description: 'A robot navigates a 3D maze to find keys, avoid traps, and reach the exit. Raycast observations, pathfinding challenge.',
-    tech: 'R3F',
-    algos: 'DQN · Q-Table',
-    accent: '#14b8a6',
-  },
-]
-
+import DEMOS from '../data/demos.json'
 export default function Demos() {
   return (
     <section id="demos" className="relative">
@@ -74,11 +14,9 @@ export default function Demos() {
               Watch agents learn in real time
             </h2>
             <p className="text-lg text-slate-400">
-              Six interactive environments showing IgnitionAI in action. Each runs in your browser, trains locally, and demonstrates a different RL challenge.
+              {DEMOS.length} interactive environments showing IgnitionAI in action. Each runs in your browser. Circuit Racing supports keyboard play against learned drivers; the other environments explore reinforcement learning.
             </p>
-            <div className="mt-3 flex justify-center">
-              <MobileInferenceBadge />
-            </div>
+
           </div>
 
           {/* Demo grid */}
diff --git a/packages/web/components/features-02.tsx b/packages/web/components/features-02.tsx
index 3692b32..1fec742 100644
--- a/packages/web/components/features-02.tsx
+++ b/packages/web/components/features-02.tsx
@@ -1,3 +1,4 @@
+import DEMOS from '../data/demos.json'
 import Particles from './particles'
 import Highlighter, { HighlighterItem } from './highlighter'
 
@@ -16,7 +17,7 @@ export default function Features02() {
           {/* Section header */}
           <div className="max-w-3xl mx-auto text-center pb-12 md:pb-20">
             <h2 className="h2 bg-clip-text text-transparent bg-linear-to-r from-slate-200/60 via-slate-200 to-slate-200/60 pb-4">Everything you need to ship RL.</h2>
-            <p className="text-lg text-slate-400">Three algorithms, auto-configuration, ONNX export, HuggingFace storage, and five working demos. All TypeScript, all open source.</p>
+            <p className="text-lg text-slate-400">Three algorithms, auto-configuration, ONNX export, HuggingFace storage, and {DEMOS.length} working demos. All TypeScript, all open source.</p>
           </div>
 
           {/* Highlighted boxes */}
@@ -386,9 +387,9 @@ export default function Features02() {
                 <svg className="shrink-0 fill-slate-300" xmlns="http://www.w3.org/2000/svg" width="16" height="16">
                   <path d="M14.574 5.67a13.292 13.292 0 0 1 1.298 1.842 1 1 0 0 1 0 .98C15.743 8.716 12.706 14 8 14a6.391 6.391 0 0 1-1.557-.2l1.815-1.815C10.97 11.82 13.06 9.13 13.82 8c-.163-.243-.39-.56-.669-.907l1.424-1.424ZM.294 15.706a.999.999 0 0 1-.002-1.413l2.53-2.529C1.171 10.291.197 8.615.127 8.49a.998.998 0 0 1-.002-.975C.251 7.29 3.246 2 8 2c1.331 0 2.515.431 3.548 1.038L14.293.293a.999.999 0 1 1 1.414 1.414l-14 14a.997.997 0 0 1-1.414 0ZM2.18 8a12.603 12.603 0 0 0 2.06 2.347l1.833-1.834A1.925 1.925 0 0 1 6 8a2 2 0 0 1 2-2c.178 0 .348.03.512.074l1.566-1.566C9.438 4.201 8.742 4 8 4 5.146 4 2.958 6.835 2.181 8Z" />
                 </svg>
-                <h4 className="font-medium text-slate-50">Five working demos</h4>
+                <h4 className="font-medium text-slate-50">{DEMOS.length} working demos</h4>
               </div>
-              <p className="text-sm text-slate-400">GridWorld, CartPole, MountainCar, CartPole 3D, and Car Circuit. Clone, run, learn.</p>
+              <p className="text-sm text-slate-400">{DEMOS.map(demo => demo.title).join(', ')}. Clone, run, learn.</p>
             </div>
           </div>
 
diff --git a/packages/web/content/_meta.js b/packages/web/content/_meta.js
index 085e79d..9688dc5 100644
--- a/packages/web/content/_meta.js
+++ b/packages/web/content/_meta.js
@@ -1,6 +1,7 @@
 export default {
   index: 'Introduction',
   quickstart: 'Quickstart',
+  demos: 'Demo catalogue',
   algorithms: 'Algorithms',
   'how-it-works': 'How it works',
   api: 'API Reference',
diff --git a/packages/web/content/algorithms/dqn.mdx b/packages/web/content/algorithms/dqn.mdx
index 484344b..2250d8f 100644
--- a/packages/web/content/algorithms/dqn.mdx
+++ b/packages/web/content/algorithms/dqn.mdx
@@ -5,7 +5,7 @@ description: A pedagogical explanation of how DQN works in IgnitionAI — replay
 
 # DQN — Deep Q-Network
 
-DQN is the first algorithm you should try. It's a good default on almost any discrete-action environment, it's sample-efficient thanks to its replay buffer, and it's the most battle-tested algorithm in modern RL. Most of the ML-Agents demos you've seen in Unity use a DQN variant.
+DQN learns action values for discrete-action environments using a replay buffer and a target network. Its results depend on the observations, reward design and training budget.
 
 This page is deliberately verbose. By the end of it, you should understand *why* DQN works, not just how to call `env.train('dqn')`.
 
@@ -53,7 +53,7 @@ In IgnitionAI, the default buffer size is **10 000 transitions**, and training s
 
 We said above that using the same network to predict *and* compute the target leads to instability — imagine trying to hit a bullseye that moves every time you pull the bow. DQN's fix is to keep a **second copy of the network** — the *target network* — that is updated less frequently. The target network is used for computing `max_a' Q(s', a')`, so the target is stable for a while.
 
-In IgnitionAI, the target network is synced to the main network every `targetUpdateFrequency` steps. The default is **1000 steps** (a few hundred episodes on most envs). You can tune this lower for faster-learning envs or higher for more stable training.
+In IgnitionAI, the target network is synced to the main network every `targetUpdateFrequency` steps. The default is **100 training updates** through `IgnitionEnv`, or **1000** when constructing `DQNAgent` directly. You can tune this lower for faster-learning envs or higher for more stable training.
 
 ### 3. Epsilon-greedy exploration
 
@@ -62,28 +62,50 @@ A greedy policy (always pick the argmax Q-value) will get stuck early. The first
 `ε` starts high (almost pure exploration) and decays over time to a small floor (almost pure exploitation). In IgnitionAI:
 
 - `epsilon = 1.0` at the start (100% random)
-- Every episode, `epsilon *= 0.995` (half-life of ~138 episodes)
+- Every successful training update, `epsilon *= 0.995` (half-life of ~138 updates)
 - `minEpsilon = 0.01` is the floor (1% random forever, to avoid hard lock-in)
 
 > **A common mistake**: if you set `minEpsilon = 0`, the agent will eventually stop exploring entirely and can't recover from misleading early estimates. Always leave a small epsilon floor.
 
 ## Default hyperparameters
 
-These are the exact values IgnitionAI uses when you call `env.train('dqn')` with no config override. Source: `packages/backend-tfjs/src/agents/dqn.ts`.
+These are the values used by `env.train('dqn')`, from `packages/backend-tfjs/src/defaults.ts`. Direct `DQNAgent` construction uses `[24, 24]` hidden layers and a target update frequency of 1000 instead.
 
 | Hyperparameter | Default | What it controls |
 |---|---|---|
-| `hiddenLayers` | `[24, 24]` | Two hidden layers of 24 units each. A tiny MLP that fits CartPole-class problems. |
+| `hiddenLayers` | `[64, 64]` | Two hidden layers of 64 units each. |
 | `lr` | `0.001` | Adam learning rate. |
 | `gamma` | `0.99` | Discount factor. |
 | `epsilon` | `1.0` | Starting exploration rate (pure random). |
-| `epsilonDecay` | `0.995` | Multiplicative decay applied every episode. |
+| `epsilonDecay` | `0.995` | Multiplicative decay applied every training update. |
 | `minEpsilon` | `0.01` | Floor — exploration never drops below 1%. |
 | `batchSize` | `32` | Minibatch size sampled from the replay buffer per update. |
 | `memorySize` | `10000` | Ring-buffer capacity for experience replay. |
-| `targetUpdateFrequency` | `1000` | Steps between target network syncs. |
+| `targetUpdateFrequency` | `100` | Training updates between target network syncs. |
 | `backend` | `'auto'` | TF.js backend — auto-selects WebGPU → WebGL → WASM → CPU. |
 
+## Double DQN
+
+Select Double DQN through the same public API:
+
+```ts
+env.train('double-dqn', { backend: 'cpu' });
+// Direct construction: new DQNAgent({ inputSize, actionSize, doubleQ: true })
+```
+
+DQN uses the maximum next-state value from the target network. Double DQN instead selects an action with the online network and evaluates that action with the target network:
+
+```text
+nextAction = argmax_a Q_online(nextState, a)
+target = reward + gamma * Q_target(nextState, nextAction)
+```
+
+Both use only the reward after a true termination and preserve bootstrap after a time-limit truncation. Replay, optimization and target synchronization are otherwise shared. This separation follows [van Hasselt, Guez and Silver, Deep Reinforcement Learning with Double Q-learning](https://arxiv.org/abs/1509.06461). It does not guarantee better results on every environment.
+
+Circuit Racing exposes both methods in its training selector. Its [fixed-budget racing comparison](/demos/car-circuit/reports/racing-q-v3/index.html) includes every seed, failure and final checkpoint.
+
+Provider-based saves include the effective `algorithm` (`dqn` or `double-dqn`) in metadata. Inference uses greedy action selection for both. To resume training, construct the matching algorithm before loading: the current storage API loads weights, not the algorithm choice or optimizer/replay state. Existing DQN calls retain their default behavior.
+
 ## Tuning guide — which knobs to turn and in what order
 
 If DQN is working, don't touch anything. If it isn't, tune in this order:
diff --git a/packages/web/content/demos.mdx b/packages/web/content/demos.mdx
new file mode 100644
index 0000000..5153cd8
--- /dev/null
+++ b/packages/web/content/demos.mdx
@@ -0,0 +1,16 @@
+---
+title: Demo catalogue
+description: The same demo routes, methods and package names used by the gallery and static build.
+---
+
+import DemoCatalog from '../components/demo-catalog'
+
+# Demo catalogue
+
+Circuit Racing is the featured desktop keyboard experience: import real 3D vehicles, train and save neural drivers, race frozen checkpoints and drive against them. Supplied imitation drivers include their evaluation failures; their competence is measured rather than assumed.
+
+<DemoCatalog />
+
+The catalogue is maintained in `packages/web/data/demos.json`. The build consumes it directly. Target Chasing is not included: its build compatibility has not been validated for this catalogue.
+
+The [oval Circuit tutorial](/docs/tutorials/car-circuit) describes the legacy constant-speed teaching environment. Its checkpoints and seven observations are incompatible with the racing demo's versioned twenty observations and nine driving actions.
diff --git a/packages/web/content/tutorials/car-circuit.mdx b/packages/web/content/tutorials/car-circuit.mdx
index 0a03b34..abc836c 100644
--- a/packages/web/content/tutorials/car-circuit.mdx
+++ b/packages/web/content/tutorials/car-circuit.mdx
@@ -5,6 +5,8 @@ description: The hero demo as a tutorial. A 3D car learns to drive an oval circu
 
 # Car Circuit
 
+> This tutorial uses the legacy oval teaching environment. The live [Circuit Racing demo](/demos/car-circuit/) now has imported vehicles, nine driving actions and twenty observations. Its learned checkpoints are incompatible with this example; see the [current catalogue](/docs/demos).
+
 This tutorial is the most ambitious one we ship. By the end you'll have a 3D car driving an oval circuit under a DQN policy you trained yourself, a chase camera that follows the car, a minimap overlay, and a speed slider from 1× to 50×. It's also the tutorial most likely to take longer than the estimate — budget real time for it.
 
 Estimated time: **50–75 minutes**.
@@ -260,7 +262,7 @@ Drag it from 1 to 50 and watch the car go from a slow crawl to a blur.
 ## Next steps
 
 - **[Export to Unity via ONNX](/docs/tutorials/onnx-unity)** — take the car policy you just trained and ship it to a Unity Sentis project.
-- **The real demo** — `pnpm --filter demo-car-circuit dev` in the monorepo runs the full polished version with minimap, HUD, trail, and pre-trained weights.
+- **The real demo** — `pnpm --filter demo-car-circuit dev` in the monorepo runs Circuit Racing with imported vehicles, local learned checkpoints and frozen-policy races.
 
 ---
 
diff --git a/packages/web/data/demos.json b/packages/web/data/demos.json
new file mode 100644
index 0000000..2df3759
--- /dev/null
+++ b/packages/web/data/demos.json
@@ -0,0 +1,91 @@
+[
+  {
+    "slug": "car-circuit",
+    "title": "Circuit Racing",
+    "description": "Train and save neural drivers, race their frozen checkpoints, or take the wheel. Two licensed 3D vehicles, three laps and shared arcade physics.",
+    "tech": "R3F",
+    "algos": "Imitation MLP · DQN · Double DQN",
+    "accent": "#e6ee58",
+    "pkg": "demo-car-circuit",
+    "dir": "packages/demo-car-circuit",
+    "featured": true,
+    "platform": "Desktop keyboard"
+  },
+  {
+    "slug": "maze",
+    "title": "Maze",
+    "description": "A low-poly humanoid trains in a Rapier 3D arena with prefab walls, sensor rays, doors, and moving hazards.",
+    "tech": "R3F · Rapier",
+    "algos": "Q-Table · DQN · PPO",
+    "accent": "#22c55e",
+    "featured": false,
+    "pkg": "demo-maze",
+    "dir": "packages/demo-maze"
+  },
+  {
+    "slug": "cartpole",
+    "title": "CartPole",
+    "description": "Classic pole-balancing benchmark with Euler physics.",
+    "tech": "2D Canvas",
+    "algos": "DQN · PPO",
+    "accent": "#3b82f6",
+    "pkg": "demo-cartpole",
+    "dir": "packages/demo-cartpole",
+    "featured": false
+  },
+  {
+    "slug": "mountaincar",
+    "title": "MountainCar",
+    "description": "Sparse reward challenge. Explore the momentum needed to reach the hilltop.",
+    "tech": "2D Canvas",
+    "algos": "DQN · PPO",
+    "accent": "#f59e0b",
+    "pkg": "demo-mountaincar",
+    "dir": "packages/demo-mountaincar",
+    "featured": false
+  },
+  {
+    "slug": "cartpole-3d",
+    "title": "CartPole 3D",
+    "description": "Classic CartPole rendered in 3D with React Three Fiber. Metallic materials and shadows.",
+    "tech": "R3F",
+    "algos": "DQN · PPO",
+    "accent": "#6366f1",
+    "pkg": "demo-cartpole-3d",
+    "dir": "packages/demo-cartpole-3d",
+    "featured": false
+  },
+  {
+    "slug": "drone-navigation",
+    "title": "Drone Navigation",
+    "description": "A quadcopter learns to fly to moving target points. Rigid-body physics, 8 thrust combos, real gravity and torque.",
+    "tech": "R3F · Physics",
+    "algos": "DQN",
+    "accent": "#a855f7",
+    "pkg": "demo-drone-navigation",
+    "dir": "packages/demo-drone-navigation",
+    "featured": false
+  },
+  {
+    "slug": "maze-3d",
+    "title": "Maze 3D",
+    "description": "A robot navigates a 3D maze to find keys, avoid traps, and reach the exit. Raycast observations, pathfinding challenge.",
+    "tech": "R3F",
+    "algos": "DQN · Q-Table",
+    "accent": "#14b8a6",
+    "pkg": "demo-maze-3d",
+    "dir": "packages/demo-maze-3d",
+    "featured": false
+  },
+  {
+    "slug": "gridworld",
+    "pkg": "demo-gridworld",
+    "dir": "packages/demo-gridworld",
+    "title": "GridWorld",
+    "description": "A discrete navigation environment for exploring rewards and learned action values.",
+    "tech": "2D Grid",
+    "algos": "Q-Table · DQN · PPO",
+    "accent": "#06b6d4",
+    "featured": false
+  }
+]
diff --git a/packages/web/scripts/build-demos.mjs b/packages/web/scripts/build-demos.mjs
index 3bf88cc..b82fbb6 100644
--- a/packages/web/scripts/build-demos.mjs
+++ b/packages/web/scripts/build-demos.mjs
@@ -6,9 +6,10 @@
  * self-contained Next.js deployment with all demos embedded.
  */
 import { execSync } from 'node:child_process'
-import { cpSync, existsSync, mkdirSync, readdirSync, rmSync, writeFileSync } from 'node:fs'
+import { cpSync, existsSync, mkdirSync, readdirSync, readFileSync, mkdtempSync, writeFileSync } from 'node:fs'
 import { dirname, join, resolve } from 'node:path'
 import { fileURLToPath } from 'node:url'
+import { promoteDemos } from './promote-demos.mjs'
 
 const __dirname = dirname(fileURLToPath(import.meta.url))
 const repoRoot = resolve(__dirname, '../../..')
@@ -19,27 +20,16 @@ console.log('[build-demos] repoRoot:', repoRoot)
 console.log('[build-demos] webPublicDemos:', webPublicDemos)
 console.log('[build-demos] cwd:', process.cwd())
 
-const DEMOS = [
-  { slug: 'maze',             pkg: 'demo-maze',             dir: 'packages/demo-maze'             },
-  { slug: 'gridworld',        pkg: 'demo-gridworld',        dir: 'packages/demo-gridworld'        },
-  { slug: 'cartpole',         pkg: 'demo-cartpole',         dir: 'packages/demo-cartpole'         },
-  { slug: 'mountaincar',      pkg: 'demo-mountaincar',      dir: 'packages/demo-mountaincar'      },
-  { slug: 'cartpole-3d',      pkg: 'demo-cartpole-3d',      dir: 'packages/demo-cartpole-3d'      },
-  { slug: 'car-circuit',      pkg: 'demo-car-circuit',      dir: 'packages/demo-car-circuit'      },
-  { slug: 'drone-navigation', pkg: 'demo-drone-navigation', dir: 'packages/demo-drone-navigation' },
-  { slug: 'maze-3d',          pkg: 'demo-maze-3d',          dir: 'packages/demo-maze-3d'          },
-]
+const DEMOS = JSON.parse(readFileSync(new URL('../data/demos.json', import.meta.url), 'utf8'))
 
 function run(cmd, env) {
   console.log(`\n$ ${cmd}`)
   execSync(cmd, { stdio: 'inherit', cwd: repoRoot, env: { ...process.env, ...env } })
 }
 
-// Clean destination
-if (existsSync(webPublicDemos)) {
-  rmSync(webPublicDemos, { recursive: true, force: true })
-}
-mkdirSync(webPublicDemos, { recursive: true })
+// Build into a fresh staging directory; preserve the previous local artifact.
+mkdirSync(join(repoRoot, '.scratch'), { recursive: true })
+const staging = mkdtempSync(join(repoRoot, '.scratch/demos-stage-'))
 
 // ---------------------------------------------------------------------------
 // 1. Build the library packages first.
@@ -75,7 +65,7 @@ for (const demo of DEMOS) {
     throw new Error(`[build-demos] expected ${distDir} to exist after build`)
   }
 
-  const outDir = join(webPublicDemos, demo.slug)
+  const outDir = join(staging, demo.slug)
   console.log(`Copying ${distDir} → ${outDir}`)
   cpSync(distDir, outDir, { recursive: true })
 }
@@ -86,7 +76,10 @@ const manifest = {
   commit: process.env.VERCEL_GIT_COMMIT_SHA || process.env.GITHUB_SHA || 'local',
   demos: DEMOS.map((d) => d.slug),
 }
-writeFileSync(join(webPublicDemos, 'manifest.json'), JSON.stringify(manifest, null, 2))
+writeFileSync(join(staging, 'manifest.json'), JSON.stringify(manifest, null, 2))
+
+mkdirSync(dirname(webPublicDemos), { recursive: true })
+promoteDemos(staging, webPublicDemos, join(repoRoot, `.scratch/demos-backup-${Date.now()}`))
 
 console.log('\n✓ All demos built and copied to packages/web/public/demos/')
 console.log('[build-demos] final listing:', readdirSync(webPublicDemos))
diff --git a/packages/web/scripts/promote-demos.mjs b/packages/web/scripts/promote-demos.mjs
new file mode 100644
index 0000000..d3d0d05
--- /dev/null
+++ b/packages/web/scripts/promote-demos.mjs
@@ -0,0 +1,16 @@
+import { existsSync, renameSync } from 'node:fs'
+
+/** Promote generated output while keeping the previous catalogue recoverable. */
+export function promoteDemos(staging, target, backup, io = { existsSync, renameSync }) {
+  const hadPrevious = io.existsSync(target)
+  if (hadPrevious) io.renameSync(target, backup)
+  try {
+    io.renameSync(staging, target)
+  } catch (error) {
+    if (hadPrevious) {
+      try { io.renameSync(backup, target) }
+      catch (restoreError) { throw new AggregateError([error, restoreError], `Promotion failed; restore previous demos from ${backup}`) }
+    }
+    throw error
+  }
+}
diff --git a/packages/web/test/catalogue.test.ts b/packages/web/test/catalogue.test.ts
new file mode 100644
index 0000000..8da5759
--- /dev/null
+++ b/packages/web/test/catalogue.test.ts
@@ -0,0 +1,13 @@
+import type { PathLike } from 'node:fs';
+import { expect,it } from 'vitest';
+import { promoteDemos } from '../scripts/promote-demos.mjs';
+it('restores the previous public demos when staging promotion fails',()=> {
+  const files=new Map([['public','previous'],['staging','new']]);
+  const io={existsSync:(path:PathLike)=>files.has(String(path)),renameSync:(fromPath:PathLike,toPath:PathLike)=>{
+    const from=String(fromPath),to=String(toPath);
+    if(from==='staging')throw new Error('injected promotion failure');
+    files.set(to,files.get(from)!);files.delete(from);
+  }};
+  expect(()=>promoteDemos('staging','public','backup',io)).toThrow('promotion failure');
+  expect(files.get('public')).toBe('previous');expect(files.get('staging')).toBe('new');
+});
diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml
index 1c188b4..c350f49 100644
--- a/pnpm-lock.yaml
+++ b/pnpm-lock.yaml
@@ -141,6 +141,9 @@ importers:
       '@react-three/fiber':
         specifier: ^9.1.2
         version: 9.1.2(@types/react@19.1.2)(immer@11.1.4)(react-dom@19.1.0(react@19.1.0))(react@19.1.0)(three@0.162.0)
+      '@tensorflow/tfjs':
+        specifier: 4.22.0
+        version: 4.22.0(encoding@0.1.13)(seedrandom@3.0.5)
       react:
         specifier: ^19.0.0
         version: 19.1.0
diff --git a/scripts/compare-racing-q.mts b/scripts/compare-racing-q.mts
new file mode 100644
index 0000000..4b55653
--- /dev/null
+++ b/scripts/compare-racing-q.mts
@@ -0,0 +1,34 @@
+import { createRequire } from 'node:module';
+import { mkdir, writeFile, readFile } from 'node:fs/promises';
+import { createHash } from 'node:crypto';
+import { execFileSync } from 'node:child_process';
+const require=createRequire(new URL('../packages/backend-tfjs/package.json',import.meta.url));
+const tf=require('@tensorflow/tfjs-node');
+import { LearnedDriver } from '../packages/demo-car-circuit/src/racing/learned-driver';
+const { trainQDriver } = createRequire(import.meta.url)('../packages/demo-car-circuit/src/racing/q-training.ts');
+import { evaluateDriver } from '../packages/demo-car-circuit/src/racing/training';
+import { Q_PROTOCOL } from '../packages/demo-car-circuit/src/racing/q-protocol';
+const folder='.scratch/circuit-racing/q-comparison-v3';
+await mkdir(folder,{recursive:true});
+const sourceFiles=['packages/demo-car-circuit/src/racing/q-training.ts','packages/demo-car-circuit/src/racing/q-protocol.ts','packages/demo-car-circuit/src/racing/learned-driver.ts','packages/demo-car-circuit/src/racing/training.ts','packages/demo-car-circuit/src/racing/driving.ts','packages/demo-car-circuit/src/racing/race.ts','packages/demo-car-circuit/src/racing/observations.ts','packages/demo-car-circuit/src/racing/reference.ts','packages/backend-tfjs/src/agents/dqn.ts','packages/backend-tfjs/src/model/BuildMLP.ts','packages/backend-tfjs/src/memory/ReplayBuffer.ts'];
+const hashes=Object.fromEntries(await Promise.all(sourceFiles.map(async p=>[p,createHash('sha256').update(await readFile(p)).digest('hex')])));
+const manifest={protocol:Q_PROTOCOL,createdAt:new Date().toISOString(),node:process.version,tfjs:tf.version.tfjs,backend:tf.getBackend(),platform:process.platform,arch:process.arch,head:execFileSync('/usr/bin/git',['rev-parse','HEAD'],{encoding:'utf8'}).trim(),sourceHashes:hashes};
+await writeFile(`${folder}/protocol.json`,JSON.stringify(manifest,null,2),{flag:'wx'});
+for(const algorithm of ['dqn','double-dqn'] as const)for(const seed of Q_PROTOCOL.seeds){
+  const initial=new LearnedDriver(seed,algorithm),start=performance.now();
+  const checkpoint=await trainQDriver(initial.exportCheckpoint(),{onProgress:(p: {round:number})=>{if(p.round%5000===0)console.log(JSON.stringify({algorithm,seed,transitions:p.round,seconds:(performance.now()-start)/1000}));}});
+  initial.dispose();
+  const trainingSeconds=(performance.now()-start)/1000;
+  await writeFile(`${folder}/${algorithm}-${seed}.json`,JSON.stringify(checkpoint));
+  const frozen=LearnedDriver.fromCheckpoint(checkpoint),before=JSON.stringify(checkpoint.weights),reports=[];
+  const evaluationStart=performance.now();
+  try{
+    for(const test of [false,true])for(const traffic of [false,true])for(const evaluationSeed of Q_PROTOCOL.evaluationSeeds){
+      reports.push(await evaluateDriver(frozen,{seed:evaluationSeed,test,traffic}));
+    }
+    if(JSON.stringify(frozen.exportCheckpoint().weights)!==before)throw new Error('Evaluation mutated weights');
+  }finally{frozen.dispose();}
+  const report={algorithm,seed,trainingSeconds,evaluationSeconds:(performance.now()-evaluationStart)/1000,transitions:checkpoint.samples,updates:checkpoint.updates,backend:tf.getBackend(),reports};
+  await writeFile(`${folder}/report-${algorithm}-${seed}.json`,JSON.stringify(report,null,2));
+  console.log(JSON.stringify({algorithm,seed,success:reports.filter(r=>r.success).length,total:reports.length,trainingSeconds}));
+}
diff --git a/scripts/report-racing-q.mjs b/scripts/report-racing-q.mjs
new file mode 100644
index 0000000..d7614e8
--- /dev/null
+++ b/scripts/report-racing-q.mjs
@@ -0,0 +1,43 @@
+import { createHash } from 'node:crypto';
+import { execFileSync } from 'node:child_process';
+import { readFile, writeFile, mkdir, copyFile } from 'node:fs/promises';
+const source='.scratch/circuit-racing/q-comparison-v3';
+const destination='packages/demo-car-circuit/src/public/reports/racing-q-v3';
+const manifest=JSON.parse(await readFile(`${source}/protocol.json`,'utf8'));
+const reports=[];
+for(const algorithm of ['dqn','double-dqn'])for(const seed of manifest.protocol.seeds){
+  const report=JSON.parse(await readFile(`${source}/report-${algorithm}-${seed}.json`,'utf8'));
+  if(report.algorithm!==algorithm||report.seed!==seed||report.reports.length!==20||report.transitions!==manifest.protocol.transitions||report.backend!==manifest.backend)throw Error('Incomplete or incompatible benchmark');
+  reports.push(report);
+}
+await mkdir(destination,{recursive:true});
+const referencePath='packages/demo-car-circuit/src/racing/reference.ts';
+const reference=await readFile(referencePath);
+if(!reference.equals(execFileSync('/usr/bin/git',['show',`${manifest.head}:${referencePath}`])))throw Error('Reference controller changed since recorded HEAD');
+const implementationCommit=execFileSync('/usr/bin/git',['rev-parse','HEAD'],{encoding:'utf8'}).trim();
+for(const [path,hash] of Object.entries(manifest.sourceHashes)){
+  if(createHash('sha256').update(execFileSync('/usr/bin/git',['show',`${implementationCommit}:${path}`])).digest('hex')!==hash)throw Error(`Committed source mismatch: ${path}`);
+}
+const machine = process.platform === 'darwin' ? {cpu:execFileSync('/usr/sbin/sysctl',['-n','machdep.cpu.brand_string'],{encoding:'utf8'}).trim(),memoryBytes:Number(execFileSync('/usr/sbin/sysctl',['-n','hw.memsize'],{encoding:'utf8'}).trim()),observedAfterRun:true} : null;
+const additionalProvenance={machine,implementationCommit,allRecordedSourcesVerifiedAtCommit:true,reference:{path:referencePath,sha256:createHash('sha256').update(reference).digest('hex'),verifiedIdenticalToRecordedHead:manifest.head,capturedAfterRun:true}};
+await writeFile(`${destination}/results.json`,JSON.stringify({manifest,additionalProvenance,reports},null,2));
+const mean=xs=>xs.reduce((a,b)=>a+b,0)/xs.length;
+const std=xs=>Math.sqrt(mean(xs.map(x=>(x-mean(xs))**2)));
+const stats=['dqn','double-dqn'].map(algorithm=>{
+ const rows=reports.filter(r=>r.algorithm===algorithm), rates=rows.map(r=>r.reports.filter(e=>e.success).length/20*100), all=rows.flatMap(r=>r.reports);
+ return {algorithm,success:all.filter(r=>r.success).length,completed:all.filter(r=>r.completed).length,mean:mean(rates),std:std(rates),min:Math.min(...rates),max:Math.max(...rates),trainingSeconds:mean(rows.map(r=>r.trainingSeconds)),evaluationSeconds:mean(rows.map(r=>r.evaluationSeconds)),meanLaps:mean(all.map(r=>r.laps))};
+});
+const f=n=>n.toFixed(1);
+const rows=reports.map(r=>`<tr><td>${r.algorithm}</td><td>${r.seed}</td><td>${r.reports.filter(e=>e.success).length}/20</td><td>${r.reports.filter(e=>e.completed).length}/20</td><td>${f(r.trainingSeconds)} s</td><td>${f(r.evaluationSeconds)} s</td><td><a href="${r.algorithm}-${r.seed}.json">Poids</a></td></tr>`).join('');
+for(const r of reports)await copyFile(`${source}/${r.algorithm}-${r.seed}.json`,`${destination}/${r.algorithm}-${r.seed}.json`);
+const html=`<!doctype html><html lang="fr"><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>DQN / Double DQN — Circuit Racing</title><style>
+*{box-sizing:border-box}body{margin:0;background:#101517;color:#e8ecea;font:16px/1.65 system-ui,sans-serif}main{max-width:1080px;margin:auto;padding:48px 24px}a{color:#e6ee58}h1{font-size:clamp(32px,5vw,54px);line-height:1.1;letter-spacing:-.04em}h2{margin-top:44px;font-size:24px}.eyebrow{color:#e6ee58;font-size:12px;letter-spacing:.16em}.muted{color:#a7b4b0}.cards{display:grid;grid-template-columns:repeat(auto-fit,minmax(230px,1fr));gap:20px}.card{padding:24px;border:1px solid #37403d}.score{font-size:44px;color:#e6ee58;font-weight:700}.scroll{overflow-x:auto}table{border-collapse:collapse;width:100%;font-variant-numeric:tabular-nums}td,th{text-align:left;padding:14px 12px;border-bottom:1px solid #37403d;white-space:nowrap}th{color:#a7b4b0;font-size:13px}code{overflow-wrap:anywhere}.note{padding:20px;border-left:3px solid #e6ee58;background:#1b2222}li{margin-bottom:8px}</style>
+<main><a href="../../">← Circuit Racing</a><p class="eyebrow">EXPÉRIENCE REPRODUCTIBLE · RACING Q-LEARNING V3</p><h1>DQN et Double DQN,<br>à budget égal.</h1><p class="muted">Cinq graines par méthode · 20 courses figées par pilote · 20 000 transitions d’entraînement · ${manifest.backend}, TensorFlow.js ${manifest.tfjs}</p>
+<div class="cards">${stats.map(s=>`<section class="card"><h2 style="margin:0">${s.algorithm==='dqn'?'DQN':'Double DQN'}</h2><div class="score">${s.success}/100</div><p>courses réussies · ${s.completed}/100 terminées</p><p class="muted">Réussite par graine : moyenne ${f(s.mean)} %, écart-type ${f(s.std)} points ; ${f(s.min)}–${f(s.max)} %.</p></section>`).join('')}</div>
+<p class="note">Ces résultats décrivent ce circuit, ce curriculum et ce budget. Ils ne démontrent pas une supériorité générale de l’un des algorithmes. Les graines d’évaluation ne varient que légèrement le cap initial ; ces courses ne constituent pas 200 scénarios indépendants. Tous les checkpoints finaux et tous les échecs sont conservés.</p>
+<h2>Chaque graine, sans sélection</h2><div class="scroll"><table><thead><tr><th>Méthode</th><th>Graine</th><th>Réussies</th><th>Terminées</th><th>Entraînement</th><th>Évaluation</th><th>Checkpoint</th></tr></thead><tbody>${rows}</tbody></table></div>
+<h2>Ce qui est identique</h2><ul><li>Réseau 20 → 32 → 32 → 9, ReLU et sorties Q linéaires ; Adam 0,001 ; replay 10 000 ; batch 32 ; gamma 0,99.</li><li>20 000 décisions, chacune maintenue six ticks physiques de 1/60 s. Mise à jour toutes les quatre transitions et copie cible toutes les 100 mises à jour. Dernier checkpoint programmé ; aucune sélection sur l’évaluation.</li><li>Exploration, replay et initialisation pilotés par la graine. Pas de mélange aléatoire supplémentaire dans fit.</li><li>Entraînement sur Alpine Park : 10 000 décisions solo, puis 10 000 avec trois références figées ; départs variés sur la piste, vitesse initiale 4–12 m/s, épisodes limités à 600 décisions. Les sorties restent récupérables avec les mêmes remises en piste et pénalités que la course. Récompense de progression, checkpoints et pénalités. Aucun label ni remplacement par un pilote à règles.</li><li>Conduite et course V2 : barrières continues, collisions voiture/voiture et voiture/rail, sortie ralentie (+2 s), remise après immobilisation dans un rayon de 1 m pendant 5 s (+5 s). Les mêmes contraintes valent dans tous les modes.</li><li>Évaluation complète : cinq caps initiaux par circuit et par condition solo/trafic. Alpine Park et Harbour Test, trois tours, plafond 300 s. Trafic constitué de références à règles figées identiques pour les deux méthodes.</li><li>Réussite : trois tours, aucune remise en piste, pénalité ≤ 10 s. Une course terminée peut échouer à ce critère.</li></ul>
+<h2>Temps et limites</h2><p>Temps réels mesurés sur ${manifest.platform}/${manifest.arch}, Node ${manifest.node}${machine ? `, ${machine.cpu}, ${machine.memoryBytes/2**30} Gio` : ""}. La machine exécutait aussi la démo navigateur ; les temps ne proviennent pas d’une machine isolée. Le premier passage inclut davantage de démarrage à froid ; les temps ne servent pas de classement de vitesse. Les départs variés du curriculum d’entraînement diffèrent des départs de course arrêtés : les échecs de transfert restent mesurés.</p><p>Les reprises navigateur rechargent les poids et recréent optimiseur, replay et exploration. Ce benchmark utilise uniquement des entraînements neufs. Les poids de cette page sont des résultats expérimentaux ; leur présence ne garantit pas un adversaire compétent.</p>
+<p><a href="results.json" download>Télécharger les 200 résultats bruts, paramètres, versions et empreintes des sources</a></p><p class="muted">Reproduction : <code>pnpm exec tsx --tsconfig scripts/tsconfig.racing.json scripts/compare-racing-q.mts</code>, puis <code>node scripts/report-racing-q.mjs</code>. Le script refuse d’écraser un protocole existant ; déplacer le dossier de résultats avant une nouvelle expérience.</p><p>Calcul Double DQN : sélection online, évaluation cible, selon <a href="https://arxiv.org/abs/1509.06461">van Hasselt, Guez et Silver</a>.</p></main></html>`;
+await writeFile(`${destination}/index.html`,html);
+console.log(JSON.stringify(stats,null,2));
diff --git a/scripts/train-racing.mts b/scripts/train-racing.mts
new file mode 100644
index 0000000..fb05b25
--- /dev/null
+++ b/scripts/train-racing.mts
@@ -0,0 +1,68 @@
+import { createRequire } from "node:module";
+import { mkdir, writeFile, readFile } from "node:fs/promises";
+import { createHash } from "node:crypto";
+import { execFileSync } from "node:child_process";
+const require = createRequire(
+  new URL("../packages/backend-tfjs/package.json", import.meta.url),
+);
+const tf = require("@tensorflow/tfjs-node");
+import { LearnedDriver } from "../packages/demo-car-circuit/src/racing/learned-driver";
+import {
+  trainDriver,
+  evaluateDriver,
+} from "../packages/demo-car-circuit/src/racing/training";
+import { LEARNING_PROTOCOL } from "../packages/demo-car-circuit/src/racing/learning-protocol";
+const seeds = (process.env.RACING_SEEDS ?? "11,29,47").split(",").map(Number);
+const rounds = Number(process.env.RACING_ROUNDS ?? 16);
+const folder =
+  process.env.RACING_OUTPUT ?? ".scratch/circuit-racing/learned-v2";
+await mkdir(folder, { recursive: true });
+const sourceFiles = ["driving", "race", "observations", "reference", "training", "learning-protocol", "learned-driver"].map(name => `packages/demo-car-circuit/src/racing/${name}.ts`);
+const sourceHashes = Object.fromEntries(await Promise.all(sourceFiles.map(async path => [path, createHash("sha256").update(await readFile(path)).digest("hex")])));
+await writeFile(`${folder}/protocol.json`, JSON.stringify({ protocol: LEARNING_PROTOCOL, seeds, rounds, sourceHashes, head: execFileSync("/usr/bin/git", ["rev-parse", "HEAD"], {encoding:"utf8"}).trim(), createdAt:new Date().toISOString(), node:process.version, tfjs:tf.version.tfjs, backend:tf.getBackend(), platform:process.platform, arch:process.arch }, null, 2), {flag:"wx"});
+for (const seed of seeds) {
+  const driver = new LearnedDriver(seed),
+    start = Date.now();
+  const untrained = await evaluateDriver(driver, { seed: 101 });
+  await trainDriver(driver, {
+    seed,
+    rounds,
+    onProgress: (p) =>
+      console.log(
+        JSON.stringify({ seed, ...p, seconds: (Date.now() - start) / 1000 }),
+      ),
+  });
+  const checkpoint = driver.exportCheckpoint();
+  await writeFile(`${folder}/driver-${seed}.json`, JSON.stringify(checkpoint));
+  const frozen = LearnedDriver.fromCheckpoint(
+    JSON.parse(JSON.stringify(checkpoint)),
+  );
+  const reports = [];
+  for (const test of [false, true])
+    for (const traffic of [false, true])
+      for (const evaluationSeed of LEARNING_PROTOCOL.evaluationSeeds) {
+        const report = await evaluateDriver(frozen, {
+          seed: evaluationSeed,
+          test,
+          traffic,
+        });
+        reports.push(report);
+        console.log(JSON.stringify({ trainingSeed: seed, ...report }));
+      }
+  await writeFile(
+    `${folder}/report-${seed}.json`,
+    JSON.stringify(
+      {
+        protocol: LEARNING_PROTOCOL,
+        rounds,
+        untrained,
+        reports,
+        seconds: (Date.now() - start) / 1000,
+      },
+      null,
+      2,
+    ),
+  );
+  frozen.dispose();
+  driver.dispose();
+}
diff --git a/scripts/tsconfig.racing.json b/scripts/tsconfig.racing.json
new file mode 100644
index 0000000..6d86ffb
--- /dev/null
+++ b/scripts/tsconfig.racing.json
@@ -0,0 +1,11 @@
+{
+  "extends": "../tsconfig.json",
+  "compilerOptions": {
+    "baseUrl": "..",
+    "paths": {
+      "@ignitionai/backend-tfjs": ["packages/backend-tfjs/src/index.ts"],
+      "@ignitionai/core": ["packages/core/src/index.ts"],
+      "@ignitionai/storage": ["packages/storage/src/index.ts"]
+    }
+  }
+}