Transcription of Algorithms for Reinforcement Learning
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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 .. ( ): 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 ..424 A catalog of Learning problems .. Closed-loop interactive Learning .. Learning in bandits .. Learning in bandits .. Learning in Markov Decision Processes .. Learning in Markov Decision Processes .. Direct methods .. in finite MDPs.
use samples to compactly represent the dynamics of the control problem. This is important for two reasons: First, it allows one to deal with learning scenarios when the dynamics is unknown. Second, even if the dynamics is available, exact reasoning that uses it might be intractable on its own. The second key idea behind RL algorithms is to use ...
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