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Plenary Speakers 
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 Tom Dietterich Learning for Sequential Decision Making Many important problems involve making a sequence of decisions. The field of "reinforcement learning" studies algorithms for learning near-optimal policies for sequential decision making. This talk will review reinforcement learning and then discuss methods for scaling up reinforcement learning algorithms to solve very large problems. These methods include some new techniques for hierarchical reinforcement learning.
Thomas G. Dietterich is Professor of Computer Science at Oregon State University. He received the Ph.D. in computer science from Stanford University in 1984, the M.S. from the University of Illinois in 1979, and the A.B. (in mathematics) from Oberlin College in 1977. He is the Executive Editor of the journal Machine Learning and the editor of the MIT Press Series on Adaptive Computation and Machine Learning. More Information Contact conald@cs.cmu.edu for more information The conference is sponsored by CMU's newly created Center for Automated Learning and Discovery. |
