Transcription of Algorithms for Reinforcement Learning
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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 .. temporal difference Learning .. methods ..36 Last update: March 12, choice of the function space ..424 A catalog of Learning problems .. Closed-loop interactive Learning .. Learning in bandits .. Learning in bandits .. Learning in markov Decision Processes .. Learning in markov Decision Processes .. Direct methods.
variables, conditional expectations, and Markov chains. It is helpful, but not necessary, for the reader to be familiar with statistical learning theory, as the essential concepts will be explained as needed. In some parts of the book, knowledge of regression techniques of machine learning will be useful. This book has three parts.
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