Transcription of Algorithms for Reinforcement Learning
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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.
The learning problems di er in the details of how the data is collected and how performance is measured. In this book, we assume that the system that we wish to control is stochastic. Further, we assume that the measurements available on the system’s state are detailed enough so
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