Learning and Sequential Decision Making
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|Offered this year?||No|
|When Offered?||Most years|
Through a combination of classic papers and more recent work, the course explores automated decision making from a computer-science perspective. It examines efficient algorithms, where they exist, for single agent and multiagent planning as well as approaches to learning near-optimal decisions from experience. Topics will include Markov decision processes, stochastic and repeated games, partially observable Markov decision processes, and reinforcement learning. Of particular interest will be issues of generalization, exploration, and representation. Each student will be expected to present a published research paper and will participate in a group project to create a reinforcement-learning system for this year's international reinforcement-learning competition. Participants should have taken a graduate-level computer science course and should have some exposure to reinforcement learning from a previous computer-science class or seminar; check with instructor if not sure.