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Monte-Carlo methods for Computation and Optimization Spring 2015

N-Grams and the Last-Good-Reply Policy in MCTS. Monte-Carlo methods for Computation and Optimization Spring 2015.

326 views • 32 slides

R-Max: A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning

R. Brafman and M. Tennenholtz Presented by Daniel Rasmussen. R-Max: A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning. Motivation. Exploration versus Exploitation

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373 views • 27 slides

FORAGING (PART II)

FORAGING (PART II). Foraging Decisions. Feeding holes (24/log). -seeds placed in 0, 6, 12, and 24 holes. After a bird samples a log, how does it decide to stay or move on?. Currency: maximize rate of intake. Foraging Decisions.

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353 views • 24 slides

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Average reward - PowerPoint PPT Presentation

Monte-Carlo methods for Computation and Optimization Spring 2015

R-Max: A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning

FORAGING (PART II)