This repository provides a comprehensive 60-day course on Deep Reinforcement Learning (DRL), targeting individuals with basic Python, PyTorch, and deep learning knowledge. It aims to demystify DRL concepts and equip learners with practical implementation skills for algorithms like Q-learning, DQN, PPO, and Actor-Critic, enabling them to tackle real-world applications.

How It Works

The course combines curated lectures from leading institutions like DeepMind and UC Berkeley with hands-on Python and PyTorch implementations of core DRL algorithms. It follows a structured weekly progression, starting from RL fundamentals (MDPs, Dynamic Programming) and advancing to value-based methods (DQN), policy gradients (REINFORCE, A2C, PPO), evolution strategies, and model-based approaches. Each week includes theoretical explanations and practical coding projects using environments like OpenAI Gym, RoboSchool, and Atari.

Quick Start & Requirements

• Install: Clone the repository and install dependencies via pip install -r requirements.txt.
• Prerequisites: Basic Python, PyTorch, and Machine Learning/Deep Learning knowledge (MLP, CNN, RNN).
• Environments: OpenAI Gym, RoboSchool, Atari games.
• Resources: Access to lectures (YouTube links provided) and course materials.

Highlighted Details

• Covers a wide range of DRL algorithms: Q-learning, DQN, DDPG, TRPO, PPO, ES, GA, MB-MF.
• Includes implementations tested on diverse environments: RoboSchool, Atari, CartPole, BipedalWalker.
• Features practical project ideas for advanced concepts and a final project.
• References key research papers and offers supplementary learning resources.

Maintenance & Community

• Active Slack channel available via email invitation for suggestions and discussions.
• Author is open to contributions and ideas.

Licensing & Compatibility

• The repository's license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking is undetermined.

Limitations & Caveats

• The README notes a "Bug Alert!" regarding strange results from the A2C implementation.
• Some advanced topics like TRPO are suggested for implementation but may be complex.
• The course structure implies a significant time commitment over 60 days.