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.
The learning problems di er in the details of how the data is collected and how performance is measured. ... pretty quickly. In fact, to keep up with the growing body of new results, Bertsekas maintains ... ground. It is here where the notation is introduced, followed by a short overview of the ...
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