This repository provides Python implementations of popular Reinforcement Learning algorithms, serving as a learning tool for students and researchers. It complements foundational RL literature and courses, offering code, exercises, and solutions for a wide range of algorithms, from dynamic programming to deep RL techniques.

How It Works

The project structures code by RL concepts and textbook chapters, integrating OpenAI Gym environments for practical application. Algorithms are implemented in Python 3, with advanced techniques leveraging TensorFlow for neural network components. This approach facilitates a structured learning path, bridging theoretical knowledge with hands-on coding experience.

Quick Start & Requirements

• Install via pip.
• Requires Python 3 and OpenAI Gym. TensorFlow is needed for advanced algorithms.
• Links to official documentation and exercises are available within each chapter's folder.

Highlighted Details

• Comprehensive coverage of RL algorithms, including Dynamic Programming, Monte Carlo, TD Learning, Q-Learning, Policy Gradients, and Deep Q-Learning.
• Exercises and solutions are provided to accompany Sutton's "Reinforcement Learning: An Introduction" and David Silver's RL course.
• Integrates with OpenAI Gym for standardized environment interaction.
• Includes implementations for both discrete and continuous action spaces.

Maintenance & Community

The repository is maintained by Denny Britz. Further community engagement details are not explicitly provided in the README.

Licensing & Compatibility

The repository's license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking would require clarification of the licensing terms.

Limitations & Caveats

Several advanced Deep Reinforcement Learning algorithms are marked as "WIP" (Work In Progress), indicating ongoing development and potential incompleteness. The lack of explicit licensing information may pose a barrier to commercial adoption.