David-Silver-Reinforcement-learning  by dalmia

This repository provides lecture notes and implementations for David Silver's Reinforcement Learning course, targeting students and practitioners seeking to understand and apply RL algorithms. It offers a structured learning path from foundational concepts to advanced techniques, with code examples to solidify understanding.

How It Works

The project pairs lecture notes with Python implementations of key RL algorithms, primarily using Keras (with TensorFlow backend) and the OpenAI Gym framework. This approach allows users to follow the course syllabus, access theoretical explanations, and immediately experiment with practical code examples for algorithms like dynamic programming, model-free prediction/control, and policy gradients.

Quick Start & Requirements

• Install dependencies: pip install tensorflow keras gym numpy
• Official course materials: Week 1

Highlighted Details

• Comprehensive coverage of David Silver's Reinforcement Learning syllabus.
• Implementations available in Keras/TensorFlow and OpenAI Gym.
• Encourages contributions for other frameworks like PyTorch.

Maintenance & Community

The repository is community-driven, with a call for Pull Requests to add or improve implementations. It references other popular RL repositories for further learning.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration into closed-source projects.

Limitations & Caveats

The primary implementations are tied to Keras/TensorFlow; other framework implementations are community contributions and may vary in completeness or quality.