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Tutorial
Model-Based Reinforcement Learning
Michael L Littman

Mon Dec 07 01:00 PM -- 03:00 PM (PST) @ Regency E/F

In model-based reinforcement learning, an agent uses its experience to construct a representation of the control dynamics of its environment. It can then predict the outcome of its actions and make decisions that maximize its learning and task performance. This tutorial will survey work in this area with an emphasis on recent results. Topics will include: Efficient learning in the PAC-MDP formalism, Bayesian reinforcement learning, models and linear function approximation, recent advances in planning.

Author Information

Michael L Littman (Rutgers University)

Michael L. Littman is professor and chair of the Department of Computer Science at Rutgers University and directs the Rutgers Laboratory for Real-Life Reinforcement Learning (RL3). His research in machine learning examines algorithms for decision making under uncertainty. Littman has earned multiple awards for teaching and his research has been recognized with three best-paper awards on the topics of meta-learning for computer crossword solving, complexity analysis of planning under uncertainty, and algorithms for efficient reinforcement learning. He has served on the editorial boards for several machine-learning journals and was Programme Co-chair of ICML 2009.

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