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An object-oriented representation for efficient reinforcement learning
Descriptive
TypeOfResource
Text
TitleInfo
(ID = T-1)
Title
An object-oriented representation for efficient reinforcement learning
Identifier
ETD_2777
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000056289
Language
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(authority = ISO639-2);
(type = code)
eng
Genre
(authority = marcgt)
theses
Subject
(ID = SBJ-1);
(authority = RUETD)
Topic
Computer Science
Subject
(ID = SBJ-2);
(authority = ETD-LCSH)
Topic
Reinforcement learning
Subject
(ID = SBJ-3);
(authority = ETD-LCSH)
Topic
Decision making--Testing
Subject
(ID = SBJ-4);
(authority = ETD-LCSH)
Topic
Markov processes
Abstract
(type = abstract)
Agents (humans, mice, computers) need to constantly make decisions to survive and thrive in their environment. In the reinforcement-learning problem, an agent needs to learn to maximize its long-term expected reward through direct interaction with the world. To achieve this goal, the agent needs to build some sort of internal representation of the relationship between its actions, the state of the world and the reward it expects to obtain. In this work, I show how the way in which the agent represents state and models the world plays a key role in its ability to learn effectively. I will introduce a new representation, based on objects and their interactions, and show how it enables several orders of magnitude faster learning on a large class of problems. I claim that this representation is a natural way of modeling state and that it bridges a gap between generality and tractability in a broad and interesting class of domains, namely those of relational nature. I will present a set of learning algorithms that make use of this representation in both deterministic and stochastic environments, and present polynomial bounds that prove their efficiency in terms of learning complexity.
PhysicalDescription
Form
(authority = gmd)
electronic resource
Extent
xiii, 133 p. : ill.
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application/pdf
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text/xml
Note
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Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Carlos Gregorio Diuk Wasser
Name
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Diuk Wasser
NamePart
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Carlos Gregorio
NamePart
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1974-
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author
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Carlos Diuk Wasser
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Littman
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Michael L
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chair
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Advisory Committee
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Michael L Littman
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(type = family)
Borghida
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Alex
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internal member
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Advisory Committee
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Alex Borghida
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(type = personal)
NamePart
(type = family)
Shan
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(type = given)
Chung-chieh
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internal member
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Advisory Committee
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Chung-chieh Shan
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(type = personal)
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(type = family)
Barto
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(type = given)
Andrew G
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outside member
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Advisory Committee
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Andrew G Barto
Name
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NamePart
Rutgers University
Role
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degree grantor
Name
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NamePart
Graduate School - New Brunswick
Role
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school
OriginInfo
DateCreated
(qualifier = exact)
2010
DateOther
(qualifier = exact);
(type = degree)
2010-10
Place
PlaceTerm
(type = code)
xx
RelatedItem
(type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
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(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T3H70FK5
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
(AUTHORITY = GS);
(ID = rulibRdec0006)
The author owns the copyright to this work.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
RightsHolder
(ID = PRH-1);
(type = personal)
Name
FamilyName
Diuk Wasser
GivenName
Carlos
Role
Copyright Holder
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Type
Permission or license
DateTime
2010-07-07 09:25:33
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Name
Carlos Diuk Wasser
Affiliation
Rutgers University. Graduate School - New Brunswick
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License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Technical
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ETD
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application/pdf
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application/x-tar
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1249280
Checksum
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