This repository serves as a curated collection of learning resources and code implementations for Reinforcement Learning (RL). It aims to provide beginners with a clear understanding of RL algorithms by offering concise, framework-agnostic code examples that highlight core algorithmic differences.

How It Works

The project focuses on providing simplified implementations of various RL algorithms, emphasizing clarity and minimal divergence between different methods. This approach allows users to quickly grasp the fundamental distinctions between algorithms like DQN, A3C, PPO, and others, facilitating a deeper understanding of their mechanics.

Quick Start & Requirements

• Install: Primarily through pip for Python dependencies.
• Prerequisites: Python, OpenAI Gym, TensorFlow, PyTorch. Specific algorithm implementations may have additional dependencies.
• Resources: Links to extensive tutorials, university courses (e.g., CS 294, David Silver's lectures), books, and blogs are provided.

Highlighted Details

• Comprehensive list of seminal RL papers with links to arXiv and Nature publications.
• Code implementations for a wide array of algorithms including DQN, DDQN, Rainbow, A2C, A3C, Policy Gradient, DDPG, TRPO, PPO, SAC, and more.
• Curated lists of resources for specific areas like Deep RL, Multi-Agent RL (MARL), and RL for NLP.
• Includes resources for advanced topics like World Models, Model-Based RL, and Imitation Learning.

Maintenance & Community

The repository is maintained by university AI researchers and includes contributions from domain experts in RL. Links to community resources are not explicitly provided in the README.

Licensing & Compatibility

The repository itself does not specify a license. Individual code implementations within the repository may have their own licenses, which would need to be checked on a per-repository basis. Compatibility for commercial use is not specified.

Limitations & Caveats

The README indicates the repository is "updating," suggesting ongoing development and potential for changes. While it aims for simplicity, the breadth of algorithms covered means some implementations might be more foundational than production-ready.