This repository provides code examples and explanations for mastering reinforcement and deep reinforcement learning, targeting practitioners and students. It offers a comprehensive guide to implementing various RL algorithms using Python, OpenAI Gym, and TensorFlow, enabling users to build intelligent agents for complex tasks.

How It Works

The project covers fundamental RL concepts like Markov Decision Processes, dynamic programming, Monte Carlo methods, and Temporal Difference learning. It then transitions to deep RL, integrating deep learning architectures such as CNNs and RNNs with RL algorithms like DQN, A3C, and PPO. This approach allows for learning from high-dimensional state spaces, such as raw pixel data from games.

Quick Start & Requirements

• Installation typically involves setting up Python environments, Anaconda, and Docker.
• Key dependencies include OpenAI Gym and TensorFlow.
• The project is structured around a book, with code examples for each chapter.
• Official documentation and the book itself serve as primary guides.

Highlighted Details

• Covers a wide range of algorithms from basic Q-learning to advanced methods like DDPG, TRPO, and PPO.
• Includes implementations for classic RL problems and game-playing agents (Atari, Doom).
• Explores recent advancements like Hindsight Experience Replay (HER) and Imagination Augmented Agents.
• Features a capstone project for car racing using DQN.

Maintenance & Community

• The repository is associated with a published book, indicating a level of curated content.
• Further community engagement details are not explicitly provided in the README.

Licensing & Compatibility

• The licensing details are not specified in the provided README.
• Compatibility for commercial use or closed-source linking would require clarification of the license.

Limitations & Caveats

The repository is primarily a companion to a book, and its direct usability as a standalone library might require further integration. The README does not specify the exact version requirements for TensorFlow or OpenAI Gym, which could lead to compatibility issues.