This repository provides code samples for the Packt book "Deep Reinforcement Learning Hands-On," targeting developers and researchers interested in applying deep learning techniques to reinforcement learning problems. It offers practical implementations of various RL algorithms and their applications in areas like game playing, trading, and chatbots.

How It Works

The project implements a wide range of deep reinforcement learning algorithms, including Q-learning, Deep Q-Networks (DQN), Policy Gradients, Actor-Critic methods, and more advanced techniques like TRPO and PPO. It leverages PyTorch for deep learning and OpenAI Gym for environment simulation, enabling users to build and train intelligent agents for diverse tasks.

Quick Start & Requirements

• Install: pip install -r requirements.txt
• Prerequisites: Python 3.6, PyTorch (version compatibility noted in README), OpenAI Gym.
• Notes: Chapter 13's OpenAI Universe is discontinued. Code may differ slightly from the book.
• Resources: Official book page: https://packt.link/free-ebook/9781838826994

Highlighted Details

• Covers 18 chapters of RL topics, from basics to advanced methods.
• Includes practical examples for stock trading, chatbots, and web navigation.
• Demonstrates algorithms like Cross-Entropy, DQN, Actor-Critic, TRPO, and PPO.
• Addresses continuous action spaces and black-box optimization in RL.

Maintenance & Community

• Maintained by the book's author, Max Lapan.
• Efforts are made to keep examples compatible with newer PyTorch and Gym versions.
• Tags and branches are used to track code changes relative to book publication.

Licensing & Compatibility

• The repository itself is not explicitly licensed in the provided text.
• Book content is published by Packt. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

Some examples, particularly from Chapter 13, rely on discontinued libraries (OpenAI Universe). The code may have minor discrepancies from the book's text due to ongoing updates and inevitable bugs.