Reinforcement Learning Course for Winter 2025

Overview and Course Structure

This course aims to teach students how autonomous systems can learn to make effective decisions, a key component of AI development. Reinforcement learning (RL) is a versatile approach used across numerous fields, including robotics, gaming, consumer behavior modeling, and healthcare. The class provides a comprehensive introduction to RL, covering fundamental challenges such as exploration and generalization. Through lectures and practical assignments, students will explore core theories, algorithms, and recent advances, including deep reinforcement learning—an emerging subfield that merges deep learning techniques with RL.

Communication and Platforms

Interactions will primarily occur through Ed discussion forums, which are encouraged for quick responses to questions. Live lectures are scheduled twice weekly on Tuesdays and Thursdays, with recordings available via Canvas. Office hours will be announced during the first week. Assignments and quizzes are managed on Gradescope, where students can also view their grades. Enrollment details and past materials are available on Canvas.

Instructors and Assistance

The course is led by Emma Brunskill, with support from a team of teaching assistants including Chethan Bhateja (Head TA), Aishwarya Mandyam, HyunJi (Alex) Nam, Hengyuan Hu, Lansong (Ryan) Li, Shiyu Zhao, and Keenon Werling.

Prerequisites

Students need to have proficiency in Python, as all assignments require coding in this language. A Python tutorial is provided for beginners. Prior programming experience in other languages (like C, C++, Matlab, or JavaScript) is acceptable. Additionally, a solid understanding of college-level calculus, linear algebra, and basic probability and statistics is necessary to grasp the course content effectively. Foundational knowledge in machine learning, particularly optimization and gradient-based methods, is recommended.

Learning Outcomes

By completing this course, students will be able to:

– Identify what sets RL apart from other AI and machine learning approaches.

– Determine when a practical problem should be formulated as an RL task, including defining its state and action spaces, dynamics, and reward models, and choosing appropriate algorithms.

– Implement standard RL algorithms in code.

– Analyze RL algorithms based on criteria such as regret, sample efficiency, computational complexity, and convergence.

– Explain the exploration-exploitation dilemma and compare different strategies for managing this trade-off.

Materials and Resources

Lecture videos and slides are available on the course website, providing detailed explanations of key concepts and strategies.

Conclusion

This course offers a rigorous foundation in reinforcement learning, preparing students to apply RL techniques across a broad range of AI-powered applications. Whether you’re interested in research or industry, the skills gained here will be crucial in developing intelligent, autonomous systems.

FAQs about Reinforcement Learning

Q: Do I need prior experience with reinforcement learning to take this course?
A: A basic understanding of machine learning and programming is recommended, but the course starts with fundamental concepts suitable for beginners.

Q: Will there be hands-on coding assignments?
A: Yes, assignments involve implementing RL algorithms in Python to reinforce theory with practice.

Q: Can I use this course to prepare for research in AI?
A: Absolutely. The course covers core RL principles along with recent advances, providing a solid foundation for further study or research.

Q: What topics are emphasized in the course?
A: Key topics include RL fundamentals, exploration strategies, deep RL, evaluation criteria, and application-specific algorithms.