This repository provides a comprehensive set of tutorials and implementations for Reinforcement Learning (RL) algorithms, ranging from foundational concepts to recent advancements. It is primarily aimed at individuals seeking to learn and experiment with RL, offering both Chinese and English resources.

How It Works

The project offers implementations of various RL algorithms, including Q-learning, Sarsa, Deep Q Network (DQN) variants (Double DQN, Prioritized Experience Replay, Dueling DQN), Policy Gradients, Actor-Critic, Deep Deterministic Policy Gradient (DDPG), A3C, Proximal Policy Optimization (PPO), and Curiosity Models like Random Network Distillation (RND). These are often demonstrated with OpenAI Gym environments.

Quick Start & Requirements

• Install via pip install -r requirements.txt.
• Requires Python 3.x and TensorFlow. Specific versions are not detailed.
• Access to OpenAI Gym environments is assumed for many examples.

Highlighted Details

• Covers a wide spectrum of RL algorithms from basic to advanced.
• Includes practical examples and experiments with environments like LunarLander and BipedalWalker.
• Offers both Chinese and English learning resources, including YouTube playlists.
• Demonstrates various RL network architectures.

Maintenance & Community

The project is maintained by MorvanZhou. Further community engagement details (Discord, Slack, etc.) are not specified in the README.

Licensing & Compatibility

The repository does not explicitly state a license. Users should assume all rights are reserved or contact the author for clarification, which may impact commercial use or closed-source linking.

Limitations & Caveats

The README does not specify TensorFlow version requirements, which could lead to compatibility issues with newer TensorFlow releases. There is no explicit mention of testing or benchmarks for the provided implementations.