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Towards Employing PSRs in a Continuous Domain.
Nicholas
K. Jong and Peter Stone.
Technical Report UT-AI-TR-04-309, The
University of Texas at Austin, Department of Computer Sciences, AI Laboratory, 2004.
UTAustin
AI Lab technical reports
(unavailable)
Predictive State Representations (PSRs) recently emerged as an alternative framework for reasoning about stochastic environments. However, unlike Markov decision processes, they have not yet been extended to large domains or domains with continuous state variables. This report briefly describes an attempt to scale PSRs to such domains. Our goal was to construct a PSR allowing an agent to track its location on the simulated soccer field used in Robocup. This line of work ended in a negative result.
@TechReport(psr-note04,
Author="Nicholas K.\ Jong and Peter Stone",
title="Towards Employing {PSR}s in a Continuous Domain",
Institution="The University of Texas at Austin, Department of Computer Sciences, AI Laboratory",
number="UT-AI-TR-04-309",
year="2004",month="February",
abstract={Predictive State Representations (PSRs) recently emerged as an alternative framework for reasoning about stochastic environments. However, unlike Markov decision processes, they have not yet been extended to large domains or domains with continuous state variables. This report briefly describes an attempt to scale PSRs to such domains. Our goal was to construct a PSR allowing an agent to track its location on the simulated soccer field used in Robocup. This line of work ended in a negative result.},
wwwnote={<a href="http://www.cs.utexas.edu/research/publications/">UT
Austin AI Lab technical reports</a>},
)
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