Learning in Partially Observable Markov Decision Processes

Learning in Partially Observable Markov Decision Processes

Indiana University-Purdue University Indianapolis (IUPUI) === Learning in Partially Observable Markov Decision process (POMDP) is motivated by the essential need to address a number of realistic problems. A number of methods exist for learning in POMDPs, but learning with limited amount of informati...

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Bibliographic Details
Main Author: Sachan, Mohit
Other Authors: Mukhopadhyay, Snehasis
Language:en_US
Published: 2013
Subjects:
Partially Observable Markov Decision Processes
Learning in POMDP
Learning automata tree
POMDP
Computer programming
Data structures (Computer science)
Stochastic systems > Research
Game theory > Mathematical models
Sequences (Mathematics)
Markov processes
Decision making > Simulation methods
User interfaces (Computer systems)
Online Access:http://hdl.handle.net/1805/3451

Internet

http://hdl.handle.net/1805/3451

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