Transcription of Algorithms for Reinforcement Learning - University of Alberta
{{id}} {{{paragraph}}}
Example: stock market
Algorithms for Reinforcement Learning - University of Alberta
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.
pseudocode of almost twenty algorithms was included, hoping that this will make it easier for the practically inclined reader to implement the algorithms described. The target audience is advanced undergaduate and graduate students, as well as researchers and practitioners who want to get a good overview of the state of the art in RL quickly.
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: