Practical_RL is an open-source course in reinforcement learning that teaches foundational and advanced RL concepts through lectures, seminars, and hands-on assignments.
The course addresses the challenge of learning reinforcement learning by combining theoretical foundations with practical implementation. It covers essential topics including decision processes, value-based methods, model-free algorithms, and deep reinforcement learning. The approach emphasizes learning by doing: each major concept is paired with a lab assignment where students implement and experiment with the ideas on concrete problems like game environments from OpenAI Gym. The materials are structured progressively, starting from introductory concepts like the cross-entropy method and moving through Q-learning, policy iteration, and deep learning integration.
The course suits self-directed learners and students in formal settings who want to build practical RL skills rather than only study theory. It works well for those who prefer learning by implementation and benefit from having curated links to deeper resources. The materials support multiple frameworks including PyTorch and TensorFlow, and can be run in Google Colab, Azure Notebooks, or locally. The course is maintained in both English and Russian, making it accessible to a broader audience. The README does not compare the course to specific alternatives, but emphasizes its philosophy of optimizing for curious learners and prioritizing practical problem-solving over pure theory.
The project maintains active engagement with its community through a git-course model that explicitly invites pull requests for improvements, corrections, and alternative implementations. The course includes a feedback form for students to report issues and suggestions, and maintains supplementary resources like an RL reading group and an online student survival guide. The materials are regularly updated and refined based on community contributions and student experience.
