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Algorithms for Reinforcement Learning - University of Alberta

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 .. temporal difference Learning .. methods ..36 Last update: March 12, choice of the function space ..424 A catalog of Learning problems .. Closed-loop interactive Learning .

a computer’s main memory. The rst algorithm explained is TD( ), which can be viewed as the learning analogue to value iteration from dynamic programming. After this, we consider the more challenging situation when there are more states than what ts into a computer’s memory. Clearly, in this case, one must compress the table representing the ...

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