Skip navigation
Skip navigation

Policy-Gradient Algorithms for Partially Observable Markov Decision Processes

Aberdeen, Douglas


Partially observable Markov decision processes are interesting because of their ability to model most conceivable real-world learning problems, for example, robot navigation, driving a car, speech recognition, stock trading, and playing games. The downside of this generality is that exact algorithms are computationally intractable. Such computational complexity motivates approximate approaches. One such class of algorithms are the so-called policy-gradient methods from reinforcement learning....[Show more]

CollectionsOpen Access Theses
Date published: 2003
Type: Thesis (PhD)
DOI: 10.25911/5d7a2b73cbb88
Access Rights: Open Access


File Description SizeFormat Image
02whole.pdf1.67 MBAdobe PDFThumbnail
01front.pdf70.37 kBAdobe PDFThumbnail

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  22 January 2019/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator