This repository is a collection of Reinforcement Learning (RL) algorithms implemented from scratch and with popular deep learning frameworks.
The goal is to build a structured reference of RL methods, ranging from the basics to advanced algorithms.
Reinforcement Learning (RL) is a field of machine learning concerned with training agents to make sequential decisions by interacting with an environment.
This repo will serve as a growing collection of RL algorithms with clean, modular code and educational explanations.
Currently, the repo is under development. Algorithms will be added periodically.
Here’s the growing list of implementations:
- Multi-Armed Bandits
- Jack's Car Problem - I
- Jack's Car Problem - II
- Gambler's Problem
Clone the repo:
git clone git@github.com:bhushansshah/RL-Implementations
cd RL-Implementations