An open course on reinforcement learning with a practical focus, featuring hands-on labs and comprehensive materials for both online and on-campus students.
Practical_RL is an open course on reinforcement learning that provides comprehensive educational materials, including lectures, seminars, and hands-on labs. It focuses on practical problem-solving and is designed to be accessible to both on-campus and online students in English and Russian.
Students, researchers, and developers interested in learning reinforcement learning through practical applications and hands-on experience with real-world problems.
It offers a community-driven, practical approach to RL education with extensive resources and flexibility for self-paced learning, distinguishing itself from theoretical-only courses.
A course in reinforcement learning in the wild
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Emphasizes practicality with labs for each major RL concept, as per the manifesto, ensuring students 'feel' ideas on real problems like Taxi-v0 or CartPole.
Includes lectures, seminars, homework, and links to additional materials like Sutton's book and blogs, optimizing for curious learners with bonus sections.
Provides clear instructions for running notebooks in Google Colab, Azure Notebooks, or locally, making it accessible without lock-in to a specific platform.
Encourages pull requests for improvements, with many contributors listed, fostering a collaborative git-course model that keeps content updated.
Admits gaps with a 'yet_another_week' section for topics not covered, leaving learners wanting more on cutting-edge methods without clear follow-ups.
Local installation requires managing dependencies via an issues thread, which can be tricky compared to the recommended Colab use, adding friction for beginners.
Assumes prior knowledge with only a deep learning recap week, lacking introductory material for absolute beginners in programming or math basics.