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.
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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