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Algorithms for Reinforcement Learning

Algorithms for Reinforcement LearningDraft of the lecture published in theSynthesis Lectures on Artificial Intelligence and Machine LearningseriesbyMorgan & Claypool PublishersCsaba Szepesv ariJune 9, 2009 Contents1 Overview32 Markov decision Preliminaries .. Markov Decision Processes .. Value functions .. Dynamic programming Algorithms for solving MDPs ..163 Value prediction Temporal difference Learning in finite state spaces .. TD(0) .. Monte-Carlo .. ( ): Unifying Monte-Carlo and TD(0) .. Algorithms for large state spaces .. ( ) with function approximation.

very few states and actions, dynamic programming is infeasible. The RL algorithms that we discuss here can be thought of as a way of turning the infeasible dynamic programming methods into practical algorithms so that they can be applied to large-scale problems. There are two key ideas that allow RL algorithms to achieve this goal. The rst idea ...

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