Transcription of Algorithms for Reinforcement Learning
{{id}} {{{paragraph}}}
Example: dental hygienist
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 .. with function approximation.
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
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: