COM SCI XLC 260R

CS 260R: Reinforcement Learning explores how autonomous agents learn to make decisions by maximizing reward in complex, uncertain environments. Topics include Markov decision processes, model-free and model-based RL, policy optimization, distributed RL systems, and applications such as game playing (e.g., AlphaGo), traffic simulation, autonomous driving, multi-agent RL, human-in-the-loop methods, and imitation learning.

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About This Course

CS 260R. Reinforcement Learning. (Instructor: Zhou, B.) Lecture, four hours; discussion, two hours; outside study, six hours. Fundamentals and advanced topics of reinforcement learning (RL), computational learning approach where agent tries to maximize total amount of reward it receives while interacting with complex and uncertain environments. Includes introduction of Markov decision processes, model-free RL and model-based RL methods, policy optimization, RL distributed system design, as well as case studies of RL in game playing such as AlphaGo, traffic simulation, autonomous driving, and other machine autonomy applications. Advanced topics of RL such as multi-agent RL, human-in-loop method, and imitation learning. Letter grading.

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Reinforcement Learning