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SUMMARY:CS Distinguished Lecture: Sham Kakade (University of Washington) – Sub-Linear Reinforcement Learning
DESCRIPTION:Speaker: Sham Kakade, University of Washington
Talk Title: Sub-Linear Reinforcement Learning
Series: Computer Science Distinguished Lecture Series
Abstract: Suppose an agent is an unknown environment and seeks to maximize his/her long term future reward. We consider the basic question: does the agent need to learn an accurate model of the environment before he/she can start executing a near-optimal long term course of actions? \n
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Specifically, this talk will consider the problem of provably optimal reinforcement learning for (episodic) finite horizon MDPs, i.e., how an agent learns to maximize his/her (long term) reward in an uncertain environment. The talk will present a novel algorithm, the Variance-reduced Upper Confidence Q-learning (vUCQ), which is the first algorithm which enjoys a regret bound that is both sub-linear in the model size and that achieves optimal minimax regret. The algorithm is sub-linear in that the time to achieve epsilon average regret is a number of samples that is far less than that required to learn any (non-trivial) estimate of the underlying model of the environment. The importance of sub-linear algorithms is largely the motivation for algorithms such as "Q-learning" and other "model-free" approaches. \n
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vUCQ is a successive refinement method in which the algorithm reduces the variance in the "Q-value" estimates and couples this estimation scheme with an upper confidence based algorithm. Technically, this coupling of these techniques is what leads to the algorithm's strong guarantees, showing that "model-free" approaches can be optimal.\n
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This lecture satisfies requirements for CSCI 591: Research Colloquium. \n
Biography: Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in the Department of Statistics and the Department of Computer Science at the University of Washington. \n
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From 2011-2015, I was a principal research scientist at Microsoft Research, New England. From 2010-2012, I was an associate professor at the Department of Statistics, Wharton, University of Pennsylvania. From 2005-2009, I was an assistant professor at the Toyota Technological Institute at Chicago. \n
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I completed my PhD at the Gatsby Computational Neuroscience Unit under the supervision of Peter Dayan, and I was an undergraduate at Caltech where I obtained my BS in physics. I was a postdoc in the Computer and Information Science department at the University of Pennsylvania under the supervision of Michael Kearns.\n
Host: Computer Science Department
DTSTART:20180320T160000
LOCATION:SAL 101
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DTEND:20180320T172000
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