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).
Figure 1: The basic reinforcement learning scenario describe the core ideas together with a large number of state of the art algorithms, followed by the discussion of …
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