Reinforcement Learning

Useful Materials for Reinforcement Learning Algorithms

Related Materials

• Spinning Up in Deep RL: This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).

• Reinforcement Learning: An Introduction (2nd Edition) & Code: This repository provides code, exercises and solutions for popular Reinforcement Learning algorithms. These are meant to serve as a learning tool to complement the theoretical materials from Reinforcement Learning: An Introduction (2nd Edition).

• CS285: Deep Reinforcement Learning: Deep Reinforcement Learning course by Sergey Levine from UC Berkeley.

• Tianshou: Tianshou (天授) is a reinforcement learning platform based on pure PyTorch. Tianshou provides a fast-speed modularized framework and pythonic API for building the deep reinforcement learning agent with the least number of lines of code.

• Stable Baselines3: Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines.

• Deep Reinforcement Learning Nanodegree: The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

• Awesome Model-Based Reinforcement Learning: This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository will be continuously updated to track the frontier of model-based rl.

• Awesome Offline Reinforcement Learning: This is a collection of research and review papers for offline reinforcement learning (offline rl).