This repository provides an open course on Reinforcement Learning (RL) tailored for practical application, targeting students and practitioners interested in hands-on experience with RL algorithms. It aims to demystify RL by focusing on core concepts, practical implementation, and "tricks of the trade," making advanced topics accessible through interactive labs and extensive external resources.

How It Works

The course structure blends lectures with practical seminars, covering foundational RL concepts like value-based methods (Q-learning, SARSA) and policy-based methods (REINFORCE, Actor-Critic). It progresses to advanced topics such as deep RL, exploration strategies, and model-based RL, emphasizing practical implementation through coding assignments. The curriculum is designed to build intuition via "feeling" concepts on practical problems, supported by links to seminal papers and blogs for deeper dives.

Quick Start & Requirements

• Installation: Primarily via Google Colab notebooks or local installation.
• Prerequisites: Python, PyTorch/TensorFlow, OpenAI Gym. Local installation is recommended for a better experience.
• Resources: Colab provides a ready-to-use environment. Local setup requires standard Python development tools.
• Links: Course Info FAQ, Online Student Survival Guide, Syllabus

Highlighted Details

• Comprehensive coverage from introductory concepts to advanced topics like POMDPs and Inverse RL.
• Hands-on labs designed to provide practical intuition for core RL algorithms.
• Strong emphasis on "practicality first," including heuristics and tricks.
• Open to contributions via pull requests for improvements and fixes.

Maintenance & Community

The course is maintained by a team from Yandex Data School and HSE, with contributions from numerous individuals listed in the README. Community interaction is facilitated through threads mentioned in the FAQ.

Licensing & Compatibility

The repository's code and materials are generally available for educational and non-commercial use, consistent with open-source courseware. Specific licensing details for individual components may vary, but the overall project encourages open contribution.

Limitations & Caveats

The syllabus is approximate, with potential shifts in lecture order and topic duration. Some advanced topics are covered briefly or linked externally, requiring self-study for deep understanding. The course focuses on specific frameworks (PyTorch/TensorFlow) for assignments.