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.
an online version of his Chapter 6 of Volume II of his book, which, at the time of writing this survey counted as much as 160 pages (Bertsekas, 2010). Other recent books on the subject include the book of Gosavi (2003) who devotes 60 pages to reinforcement learning
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