diff --git a/LeanBandits.lean b/LeanBandits.lean
index 8d2c2a9f..ab3c610e 100644
--- a/LeanBandits.lean
+++ b/LeanBandits.lean
@@ -2,4 +2,5 @@ import LeanBandits.AlgorithmBuilding
 import LeanBandits.Bandit
 import LeanBandits.ETC
 import LeanBandits.Regret
+import LeanBandits.RewardByCountMeasure
 import LeanBandits.UCB
diff --git a/LeanBandits/Bandit.lean b/LeanBandits/Bandit.lean
index db98fee1..eb25efc4 100644
--- a/LeanBandits/Bandit.lean
+++ b/LeanBandits/Bandit.lean
@@ -110,15 +110,27 @@ def hist (n : ℕ) (h : ℕ → α × R) : Iic n → α × R := fun i ↦ h i
 @[fun_prop]
 lemma measurable_arm (n : ℕ) : Measurable (arm n (α := α) (R := R)) := by unfold arm; fun_prop
 
+@[fun_prop]
+lemma measurable_arm_prod : Measurable (fun p : ℕ × (ℕ → α × R) ↦ arm p.1 p.2) := by
+  refine measurable_from_prod_countable_right fun n ↦ ?_
+  simp only
+  fun_prop
+
 @[fun_prop]
 lemma measurable_reward (n : ℕ) : Measurable (reward n (α := α) (R := R)) := by
   unfold reward; fun_prop
 
+@[fun_prop]
+lemma measurable_reward_prod : Measurable (fun p : ℕ × (ℕ → α × R) ↦ reward p.1 p.2) := by
+  refine measurable_from_prod_countable_right fun n ↦ ?_
+  simp only
+  fun_prop
+
 @[fun_prop]
 lemma measurable_hist (n : ℕ) : Measurable (hist n (α := α) (R := R)) := by unfold hist; fun_prop
 
 /-- Filtration of the bandit process. -/
-def ℱ (α : Type*) [MeasurableSpace α] :
+def ℱ (α R : Type*) [MeasurableSpace α] [MeasurableSpace R] :
     Filtration ℕ (inferInstance : MeasurableSpace (ℕ → α × R)) :=
   MeasureTheory.Filtration.piLE (X := fun _ ↦ α × R)
 
diff --git a/LeanBandits/Regret.lean b/LeanBandits/Regret.lean
index 27b45389..ecd03cb6 100644
--- a/LeanBandits/Regret.lean
+++ b/LeanBandits/Regret.lean
@@ -17,7 +17,8 @@ open scoped ENNReal NNReal
 
 namespace Bandits
 
-variable {α : Type*} {mα : MeasurableSpace α} {ν : Kernel α ℝ} {k : ℕ → α} {t : ℕ} {a : α}
+variable {α : Type*} [DecidableEq α] {mα : MeasurableSpace α} {ν : Kernel α ℝ}
+  {k : ℕ → α} {m n t : ℕ} {a : α} {h : ℕ → α × ℝ}
 
 /-! ### Definitions of regret, gaps, pull counts -/
 
@@ -30,13 +31,17 @@ def regret (ν : Kernel α ℝ) (k : ℕ → α) (t : ℕ) : ℝ :=
 noncomputable
 def gap (ν : Kernel α ℝ) (a : α) : ℝ := (⨆ i, (ν i)[id]) - (ν a)[id]
 
+omit [DecidableEq α] in
 lemma gap_nonneg [Fintype α] : 0 ≤ gap ν a := by
   rw [gap, sub_nonneg]
   exact le_ciSup (f := fun i ↦ (ν i)[id]) (by simp) a
 
-open Classical in
 /-- Number of times arm `a` was pulled up to time `t` (excluding `t`). -/
-noncomputable def pullCount (k : ℕ → α) (a : α) (t : ℕ) : ℕ := #(filter (fun s ↦ k s = a) (range t))
+noncomputable def pullCount [DecidableEq α] (k : ℕ → α) (a : α) (t : ℕ) : ℕ :=
+  #(filter (fun s ↦ k s = a) (range t))
+
+@[simp]
+lemma pullCount_zero (k : ℕ → α) (a : α) : pullCount k a 0 = 0 := by simp [pullCount]
 
 open Classical in
 lemma monotone_pullCount (k : ℕ → α) (a : α) : Monotone (pullCount k a) :=
@@ -46,14 +51,53 @@ lemma pullCount_eq_pullCount_add_one (k : ℕ → α) (t : ℕ) :
     pullCount k (k t) (t + 1) = pullCount k (k t) t + 1 := by
   simp [pullCount, range_succ, filter_insert]
 
-lemma pullCount_eq_pullCount (k : ℕ → α) (a : α) (t : ℕ) (h : k t ≠ a) :
-    pullCount k a (t + 1) = pullCount k a t := by
+lemma pullCount_eq_pullCount (h : k t ≠ a) :  pullCount k a (t + 1) = pullCount k a t := by
   simp [pullCount, range_succ, filter_insert, h]
 
+lemma pullCount_eq_sum (k : ℕ → α) (a : α) (t : ℕ) :
+    pullCount k a t = ∑ s ∈ range t, if k s = a then 1 else 0 := by simp [pullCount]
+
 /-- Number of steps until arm `a` was pulled exactly `m` times. -/
 noncomputable
 def stepsUntil (k : ℕ → α) (a : α) (m : ℕ) : ℕ∞ := sInf ((↑) '' {s | pullCount k a (s + 1) = m})
 
+lemma stepsUntil_eq_top_iff : stepsUntil k a m = ⊤ ↔ ∀ s, pullCount k a (s + 1) ≠ m := by
+  simp [stepsUntil, sInf_eq_top]
+
+lemma stepsUntil_zero_of_ne (hka : k 0 ≠ a) : stepsUntil k a 0 = 0 := by
+  unfold stepsUntil
+  simp_rw [← bot_eq_zero, sInf_eq_bot, bot_eq_zero]
+  intro n hn
+  refine ⟨0, ?_, hn⟩
+  simp only [Set.mem_image, Set.mem_setOf_eq, Nat.cast_eq_zero, exists_eq_right, zero_add]
+  rw [← zero_add 1, pullCount_eq_pullCount hka]
+  simp
+
+lemma stepsUntil_zero_of_eq (hka : k 0 = a) : stepsUntil k a 0 = ⊤ := by
+  rw [stepsUntil_eq_top_iff]
+  suffices 0 < pullCount k a 1 by
+    intro n hn
+    refine lt_irrefl 0 ?_
+    exact this.trans_le (le_trans (monotone_pullCount _ _ (by omega)) hn.le)
+  rw [← hka, ← zero_add 1, pullCount_eq_pullCount_add_one]
+  simp
+
+lemma stepsUntil_eq_dite (k : ℕ → α) (a : α) (m : ℕ) [Decidable (∃ s, pullCount k a (s + 1) = m)] :
+    stepsUntil k a m =
+      if h : ∃ s, pullCount k a (s + 1) = m then (Nat.find h : ℕ∞) else ⊤ := by
+  unfold stepsUntil
+  split_ifs with h
+  · refine le_antisymm ?_ ?_
+    · refine sInf_le ?_
+      simpa using Nat.find_spec h
+    · simp only [le_sInf_iff, Set.mem_image, Set.mem_setOf_eq, forall_exists_index, and_imp,
+        forall_apply_eq_imp_iff₂, Nat.cast_le, Nat.find_le_iff]
+      exact fun n hn ↦ ⟨n, le_rfl, hn⟩
+  · push_neg at h
+    suffices {s | pullCount k a (s + 1) = m} = ∅ by simp [this]
+    ext s
+    simpa using (h s)
+
 lemma stepsUntil_pullCount_le (k : ℕ → α) (a : α) (t : ℕ) :
     stepsUntil k a (pullCount k a (t + 1)) ≤ t := by
   rw [stepsUntil]
@@ -66,6 +110,26 @@ lemma stepsUntil_pullCount_eq (k : ℕ → α) (t : ℕ) :
     simpa [stepsUntil, pullCount_eq_pullCount_add_one]
   exact fun t' h ↦ Nat.le_of_lt_succ ((monotone_pullCount k (k t)).reflect_lt (h ▸ lt_add_one _))
 
+lemma arm_stepsUntil (hm : m ≠ 0) (h_exists : ∃ s, pullCount (arm · h) a (s + 1) = m) :
+    arm (stepsUntil (arm · h) a m).toNat h = a := by
+  classical
+  simp only [stepsUntil_eq_dite, h_exists, ↓reduceDIte, ENat.toNat_coe]
+  have h_spec := Nat.find_spec h_exists
+  have h_spec' n := Nat.find_min h_exists (m := n)
+  by_cases h_zero : Nat.find h_exists = 0
+  · simp only [h_zero, zero_add, not_lt_zero', IsEmpty.forall_iff, implies_true] at *
+    by_contra h_ne
+    rw [← zero_add 1, pullCount_eq_pullCount h_ne] at h_spec
+    simp only [pullCount_zero] at h_spec
+    exact hm h_spec.symm
+  have h_pos : 0 < Nat.find h_exists := Nat.pos_of_ne_zero h_zero
+  by_contra h_ne
+  refine h_spec' (Nat.find h_exists - 1) ?_ ?_
+  · simp [h_pos]
+  rw [Nat.sub_add_cancel (by omega)]
+  rwa [← pullCount_eq_pullCount]
+  exact h_ne
+
 /-- Reward obtained when pulling arm `a` for the `m`-th time. -/
 noncomputable
 def rewardByCount (a : α) (m : ℕ) (h : ℕ → α × ℝ) (z : ℕ → α → ℝ) : ℝ :=
@@ -73,11 +137,18 @@ def rewardByCount (a : α) (m : ℕ) (h : ℕ → α × ℝ) (z : ℕ → α →
   | ⊤ => z m a
   | (n : ℕ) => reward n h
 
+lemma rewardByCount_eq_ite (a : α) (m : ℕ) (h : ℕ → α × ℝ) (z : ℕ → α → ℝ) :
+    rewardByCount a m h z =
+      if (stepsUntil (arm · h) a m) = ⊤ then z m a
+      else reward (stepsUntil (arm · h) a m).toNat h := by
+  unfold rewardByCount
+  cases stepsUntil (arm · h) a m <;> simp
+
 lemma rewardByCount_pullCount_add_one_eq_reward (t : ℕ) (h : ℕ → α × ℝ) (z : ℕ → α → ℝ) :
     rewardByCount (arm t h) (pullCount (arm · h) (arm t h) t + 1) h z = reward t h := by
   rw [rewardByCount, ← pullCount_eq_pullCount_add_one, stepsUntil_pullCount_eq]
 
-lemma sum_rewardByCount_eq_sum_reward [DecidableEq α]
+lemma sum_rewardByCount_eq_sum_reward
     (a : α) (t : ℕ) (h : ℕ → α × ℝ) (z : ℕ → α → ℝ) :
     ∑ m ∈ Icc 1 (pullCount (arm · h) a t), rewardByCount a m h z =
       ∑ s ∈ range t, if (arm s h) = a then (reward s h) else 0 := by
@@ -87,7 +158,7 @@ lemma sum_rewardByCount_eq_sum_reward [DecidableEq α]
   · rw [← hta] at ht ⊢
     rw [pullCount_eq_pullCount_add_one, sum_Icc_succ_top (Nat.le_add_left 1 _), ht]
     rw [sum_range_succ, if_pos rfl, rewardByCount_pullCount_add_one_eq_reward]
-  · rwa [pullCount_eq_pullCount _ _ _ hta, sum_range_succ, if_neg hta, add_zero]
+  · rwa [pullCount_eq_pullCount hta, sum_range_succ, if_neg hta, add_zero]
 
 lemma sum_pullCount_mul [Fintype α] (k : ℕ → α) (f : α → ℝ) (t : ℕ) :
     ∑ a, pullCount k a t * f a = ∑ s ∈ range t, f (k s) := by
@@ -116,10 +187,12 @@ variable [Fintype α] [Nonempty α]
 noncomputable def bestArm (ν : Kernel α ℝ) : α :=
   (exists_max_image univ (fun a ↦ (ν a)[id]) (univ_nonempty_iff.mpr inferInstance)).choose
 
+omit [DecidableEq α] in
 lemma le_bestArm (a : α) : (ν a)[id] ≤ (ν (bestArm ν))[id] :=
   (exists_max_image univ (fun a ↦ (ν a)[id])
     (univ_nonempty_iff.mpr inferInstance)).choose_spec.2 _ (mem_univ a)
 
+omit [DecidableEq α] in
 lemma gap_eq_bestArm_sub : gap ν a = (ν (bestArm ν))[id] - (ν a)[id] := by
   rw [gap]
   congr
diff --git a/LeanBandits/RewardByCountMeasure.lean b/LeanBandits/RewardByCountMeasure.lean
new file mode 100644
index 00000000..8f948cfa
--- /dev/null
+++ b/LeanBandits/RewardByCountMeasure.lean
@@ -0,0 +1,173 @@
+/-
+Copyright (c) 2025 Rémy Degenne. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: Rémy Degenne
+-/
+import LeanBandits.Bandit
+import LeanBandits.Regret
+
+/-! # Laws of `stepsUntil` and `rewardByCount`
+-/
+
+open MeasureTheory ProbabilityTheory Finset
+open scoped ENNReal NNReal
+
+section Aux
+
+variable {α β γ Ω Ω' : Type*} [MeasurableSpace Ω] [StandardBorelSpace Ω] [Nonempty Ω]
+  {mα : MeasurableSpace α} {μ : Measure α} {mβ : MeasurableSpace β} {mγ : MeasurableSpace γ}
+  [MeasurableSpace Ω'] [StandardBorelSpace Ω'] [Nonempty Ω']
+  {X : α → β} {Y : α → Ω} {Z : α → Ω'}
+
+lemma MeasureTheory.Measure.comp_congr {κ η : Kernel α β} (h : ∀ᵐ a ∂μ, κ a = η a) :
+    κ ∘ₘ μ = η ∘ₘ μ :=
+  Measure.bind_congr_right h
+
+lemma MeasureTheory.Measure.copy_comp_map (hX : AEMeasurable X μ) :
+    Kernel.copy β ∘ₘ (μ.map X) = μ.map (fun a ↦ (X a, X a)) := by
+  rw [Kernel.copy, deterministic_comp_eq_map, AEMeasurable.map_map_of_aemeasurable (by fun_prop) hX]
+  congr
+
+lemma MeasureTheory.Measure.compProd_deterministic [SFinite μ] (hX : Measurable X) :
+    μ ⊗ₘ (Kernel.deterministic X hX) = μ.map (fun a ↦ (a, X a)) := by
+  rw [Measure.compProd_eq_comp_prod, Kernel.id, Kernel.deterministic_prod_deterministic,
+    Measure.deterministic_comp_eq_map]
+  rfl
+
+lemma ProbabilityTheory.condDistrib_comp_map [IsFiniteMeasure μ]
+    (hX : AEMeasurable X μ) (hY : AEMeasurable Y μ) :
+    condDistrib Y X μ ∘ₘ (μ.map X) = μ.map Y := by
+  rw [← Measure.snd_compProd, compProd_map_condDistrib hY, Measure.snd_map_prodMk₀ hX]
+
+lemma ProbabilityTheory.condDistrib_comp [IsFiniteMeasure μ]
+    (hX : AEMeasurable X μ) {f : β → Ω} (hf : Measurable f) :
+    condDistrib (f ∘ X) X μ =ᵐ[μ.map X] Kernel.deterministic f hf := by
+  rw [← Kernel.compProd_eq_iff, compProd_map_condDistrib (by fun_prop),
+    Measure.compProd_deterministic, AEMeasurable.map_map_of_aemeasurable (by fun_prop) hX]
+  congr
+
+lemma ProbabilityTheory.condDistrib_const [IsFiniteMeasure μ]
+    (hX : AEMeasurable X μ) (c : Ω) :
+    condDistrib (fun _ ↦ c) X μ =ᵐ[μ.map X] Kernel.deterministic (fun _ ↦ c) (by fun_prop) := by
+  have : (fun _ : α ↦ c) = (fun _ : β ↦ c) ∘ X := rfl
+  conv_lhs => rw [this]
+  filter_upwards [condDistrib_comp hX (by fun_prop : Measurable (fun _ ↦ c))] with b hb
+  rw [hb]
+
+@[fun_prop]
+lemma Measurable.coe_nat_enat {f : α → ℕ} (hf : Measurable f) :
+    Measurable (fun a ↦ (f a : ℕ∞)) := Measurable.comp (by fun_prop) hf
+
+@[fun_prop]
+lemma Measurable.toNat {f : α → ℕ∞} (hf : Measurable f) : Measurable (fun a ↦ (f a).toNat) :=
+  Measurable.comp (by fun_prop) hf
+
+end Aux
+
+namespace Bandits
+
+variable {α : Type*} {mα : MeasurableSpace α} [DecidableEq α] [MeasurableSingletonClass α]
+
+@[fun_prop]
+lemma measurable_pullCount (a : α) (t : ℕ) : Measurable (fun k ↦ pullCount k a t) := by
+  simp_rw [pullCount_eq_sum]
+  have h_meas s : Measurable (fun k : ℕ → α ↦ if k s = a then 1 else 0) := by
+    refine Measurable.ite ?_ (by fun_prop) (by fun_prop)
+    exact (measurableSet_singleton _).preimage (by fun_prop)
+  fun_prop
+
+@[fun_prop]
+lemma measurable_stepsUntil (a : α) (m : ℕ) : Measurable (fun k ↦ stepsUntil k a m) := by
+  classical
+  have h_union : {k' | ∃ s, pullCount k' a (s + 1) = m}
+      = ⋃ s : ℕ, {k' | pullCount k' a (s + 1) = m} := by ext; simp
+  have h_meas_set : MeasurableSet {k' | ∃ s, pullCount k' a (s + 1) = m} := by
+    rw [h_union]
+    exact MeasurableSet.iUnion fun s ↦ (measurableSet_singleton _).preimage (by fun_prop)
+  simp_rw [stepsUntil_eq_dite]
+  suffices Measurable fun k ↦ if h : k ∈ {k' | ∃ s, pullCount k' a (s + 1) = m}
+      then (Nat.find h : ℕ∞) else ⊤ by convert this
+  refine Measurable.dite (s := {k' : ℕ → α | ∃ s, pullCount k' a (s + 1) = m})
+    (f := fun x ↦ (Nat.find x.2 : ℕ∞)) (g := fun _ ↦ ⊤) ?_ (by fun_prop) h_meas_set
+  refine Measurable.coe_nat_enat ?_
+  refine measurable_find _ fun k ↦ ?_
+  suffices MeasurableSet {x : ℕ → α | pullCount x a (k + 1) = m} by
+    have : Subtype.val ''
+          {x : {k' : ℕ → α | ∃ s, pullCount k' a (s + 1) = m} | pullCount x a (k + 1) = m}
+        = {x : ℕ → α | pullCount x a (k + 1) = m} := by
+      ext x
+      simp only [Set.mem_setOf_eq, Set.coe_setOf, Set.mem_image, Subtype.exists, exists_and_left,
+        exists_prop, exists_eq_right_right, and_iff_left_iff_imp]
+      exact fun h ↦ ⟨_, h⟩
+    refine (MeasurableEmbedding.subtype_coe h_meas_set).measurableSet_image.mp ?_
+    rw [this]
+    exact (measurableSet_singleton _).preimage (by fun_prop)
+  exact (measurableSet_singleton _).preimage (by fun_prop)
+
+lemma measurable_stepsUntil'' (a : α) (m : ℕ) :
+    Measurable (fun ω : (ℕ → α × ℝ) ↦ stepsUntil (arm · ω) a m) :=
+  (measurable_stepsUntil a m).comp (by fun_prop)
+
+lemma measurable_stepsUntil' (a : α) (m : ℕ) :
+    Measurable (fun ω : (ℕ → α × ℝ) × (ℕ → α → ℝ) ↦ stepsUntil (arm · ω.1) a m) :=
+  (measurable_stepsUntil'' a m).comp measurable_fst
+
+@[fun_prop]
+lemma measurable_rewardByCount (a : α) (m : ℕ) :
+    Measurable (fun ω : (ℕ → α × ℝ) × (ℕ → α → ℝ) ↦ rewardByCount a m ω.1 ω.2) := by
+  simp_rw [rewardByCount_eq_ite]
+  refine Measurable.ite ?_ ?_ ?_
+  · exact (measurableSet_singleton _).preimage <| measurable_stepsUntil' a m
+  · fun_prop
+  · change Measurable ((fun p : ℕ × (ℕ → α × ℝ) ↦ reward p.1 p.2)
+      ∘ (fun ω : (ℕ → α × ℝ) × (ℕ → α → ℝ) ↦ ((stepsUntil (arm · ω.1) a m).toNat, ω.1)))
+    have : Measurable fun ω : (ℕ → α × ℝ) × (ℕ → α → ℝ) ↦
+        ((stepsUntil (arm · ω.1) a m).toNat, ω.1) :=
+      (measurable_stepsUntil' a m).toNat.prodMk (by fun_prop)
+    exact Measurable.comp (by fun_prop) this
+
+lemma condDistrib_rewardByCount_stepsUntil [StandardBorelSpace α] [Nonempty α]
+    {alg : Algorithm α ℝ} {ν : Kernel α ℝ} [IsMarkovKernel ν] (a : α) (m : ℕ) (hm : m ≠ 0) :
+    condDistrib (fun ω ↦ rewardByCount a m ω.1 ω.2) (fun ω ↦ stepsUntil (arm · ω.1) a m)
+        (Bandit.measure alg ν)
+      =ᵐ[(Bandit.measure alg ν).map (fun ω ↦ stepsUntil (arm · ω.1) a m)] Kernel.const _ (ν a) := by
+  sorry
+
+/-- The reward received at the `m`-th pull of arm `a` has law `ν a`. -/
+lemma hasLaw_rewardByCount [StandardBorelSpace α] [Nonempty α]
+    {alg : Algorithm α ℝ} {ν : Kernel α ℝ} [IsMarkovKernel ν] (a : α) (m : ℕ) (hm : m ≠ 0):
+    HasLaw (fun ω ↦ rewardByCount a m ω.1 ω.2) (ν a) (Bandit.measure alg ν) where
+  map_eq := by
+    have h_condDistrib :
+        condDistrib (fun ω ↦ rewardByCount a m ω.1 ω.2) (fun ω ↦ stepsUntil (arm · ω.1) a m)
+          (Bandit.measure alg ν)
+        =ᵐ[(Bandit.measure alg ν).map (fun ω ↦ stepsUntil (arm · ω.1) a m)]
+          Kernel.const _ (ν a) := condDistrib_rewardByCount_stepsUntil a m hm
+    calc (Bandit.measure alg ν).map (fun ω ↦ rewardByCount a m ω.1 ω.2)
+    _ = (condDistrib (fun ω ↦ rewardByCount a m ω.1 ω.2) (fun ω ↦ stepsUntil (arm · ω.1) a m)
+          (Bandit.measure alg ν))
+        ∘ₘ ((Bandit.measure alg ν).map (fun ω ↦ stepsUntil (arm · ω.1) a m)) := by
+      rw [condDistrib_comp_map (by fun_prop) (by fun_prop)]
+    _ = (Kernel.const _ (ν a))
+        ∘ₘ ((Bandit.measure alg ν).map (fun ω ↦ stepsUntil (arm · ω.1) a m)) :=
+      Measure.comp_congr h_condDistrib
+    _ = ν a := by
+      have : IsProbabilityMeasure
+          ((Bandit.measure alg ν).map (fun ω ↦ stepsUntil (arm · ω.1) a m)) :=
+        isProbabilityMeasure_map (by fun_prop)
+      simp
+
+lemma identDistrib_rewardByCount [StandardBorelSpace α] [Nonempty α]
+    {alg : Algorithm α ℝ} {ν : Kernel α ℝ} [IsMarkovKernel ν] (a : α) (n m : ℕ)
+    (hn : n ≠ 0) (hm : m ≠ 0) :
+    IdentDistrib (fun ω ↦ rewardByCount a n ω.1 ω.2) (fun ω ↦ rewardByCount a m ω.1 ω.2)
+      (Bandit.measure alg ν) (Bandit.measure alg ν) where
+  aemeasurable_fst := by fun_prop
+  aemeasurable_snd := by fun_prop
+  map_eq := by rw [(hasLaw_rewardByCount a n hn).map_eq, (hasLaw_rewardByCount a m hm).map_eq]
+
+lemma iIndepFun_rewardByCount (alg : Algorithm α ℝ) (ν : Kernel α ℝ) [IsMarkovKernel ν] :
+    iIndepFun (fun (p : α × ℕ) ω ↦ rewardByCount p.1 p.2 ω.1 ω.2) (Bandit.measure alg ν) := by
+  sorry
+
+end Bandits