This repository provides the code examples for "Deep Reinforcement Learning Hands-On, Third Edition," a practical guide to reinforcement learning (RL) concepts and implementations. It targets individuals seeking to understand and apply RL techniques, from foundational algorithms like Q-learning to advanced methods such as PPO and RLHF, using PyTorch and OpenAI Gym.

How It Works

The book and its code demonstrate RL algorithms through diverse applications including game playing, stock trading, and web navigation. It emphasizes a practical, hands-on approach, implementing many methods from scratch while leveraging key libraries like PyTorch, Gymnasium, and PTAN for building and training RL agents. This approach aims to provide both practical know-how and theoretical grounding for modern RL research.

Quick Start & Requirements

• Installation: Primarily uses pip with a requirements.txt file.
• Prerequisites: Python 3.11, NumPy, OpenCV, Gymnasium (with Atari, classic-control, accept-rom-license extras), PyTorch (2.5.0), Torchvision (0.20.0), PyTorch Ignite, TensorBoard, mypy, PTAN, Stable-Baselines3, TorchRL, Ray, and pytest.
• Hardware: A CUDA-enabled GPU is highly recommended for reasonable training times, though CPU execution is possible but significantly slower. Cloud GPU access (AWS, GCP, Google Colab) is suggested as an alternative.
• Resources: Setup involves installing numerous Python packages. Training times vary drastically based on the complexity of the RL task and available hardware.
• Links: Gymnasium, PyTorch Ignite, PTAN.

Highlighted Details

• Covers a wide range of RL algorithms: Cross-entropy, DQN, Actor-Critic (A2C, A3C), TRPO, PPO, DDPG, D4PG, MuZero.
• Includes practical examples for Atari games, stock trading, TextWorld, web navigation, and discrete optimization.
• Features new content on RLHF and LLMs.
• Utilizes custom implementations of building blocks via the PTAN library.

Maintenance & Community

The repository is associated with Packt Publishing and author Maxim Lapan. No specific community channels (Discord/Slack) or active maintenance signals are detailed in the README.

Licensing & Compatibility

The repository itself does not specify a license. The included libraries have various open-source licenses (e.g., MIT for PyTorch, BSD for Gymnasium). Compatibility for commercial use depends on the licenses of the individual libraries and the book's content.

Limitations & Caveats

The code is tested with specific versions of dependencies (Python 3.11, PyTorch 2.5.0), and different versions might require adjustments. The README strongly advises against using versions other than those specified for optimal results.