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State abstraction in reinforcement learning

Bartosz Papis

Abstract

This work concerns state abstraction - one of commonly proposed solutions to the curse of dimensionality problem. A particular type of state abstraction - state space abstraction is analyzed as a variable selection issue. As an effect of this analysis, an incremental state abstraction algorithm is introduced, inspired by the notions of stimulus discrimination, ambiguity and closure from behavioral psychology. This algorithm correctly solves the variable selection problem by including or removing variables one by one. It is the first among existing solutions to work not only for discrete problems, but also continuous ones.
Record ID
WUT113e3e486cfe4a4f9971e1d0d5e4db00
Diploma type
Doctor of Philosophy
Author
Bartosz Papis Bartosz Papis,, The Institute of Control and Computation Engineering (FEIT/AK)Faculty of Electronics and Information Technology (FEIT)
Title in Polish
Abstrakcja stanu w uczeniu ze wzmacnianiem
Title in English
State abstraction in reinforcement learning
Language
(en) English
Certifying Unit
Faculty of Electronics and Information Technology (FEIT)
Discipline
automation and robotics / (technology domain) / (technological sciences)
Status
Finished
Defense Date
17-11-2015
Title date
24-11-2015
Supervisor
Internal reviewers
External reviewers
Jacek Koronacki Jacek Koronacki,, Undefined Affiliation
Honored
yes
Pages
149
Keywords in English
state abstraction, Reinforcement Learning
Abstract in English
This work concerns state abstraction - one of commonly proposed solutions to the curse of dimensionality problem. A particular type of state abstraction - state space abstraction is analyzed as a variable selection issue. As an effect of this analysis, an incremental state abstraction algorithm is introduced, inspired by the notions of stimulus discrimination, ambiguity and closure from behavioral psychology. This algorithm correctly solves the variable selection problem by including or removing variables one by one. It is the first among existing solutions to work not only for discrete problems, but also continuous ones.
Thesis file
  • File: 1
    bpapis thesis.pdf
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Uniform Resource Identifier
https://repo.pw.edu.pl/info/phd/WUT113e3e486cfe4a4f9971e1d0d5e4db00/
URN
urn:pw-repo:WUT113e3e486cfe4a4f9971e1d0d5e4db00

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