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CS 332: Advanced Survey of Reinforcement Learning

This class will provide a core overview of essential topics and new research frontiers in reinforcement learning. Planned topics include: model free and model based reinforcement learning, policy search, Monte Carlo Tree Search planning methods, off policy evaluation, exploration, imitation learning, temporal abstraction/hierarchical approaches, safety and risk sensitivity, human-in-the-loop RL, inverse reinforcement learning, learning to communicate, and insights from human learning. Students are expected to create an original research paper on a related topic. Prerequisites: CS221 or AA238/CS238 or CS234 or CS229 or similar experience.
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
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