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Bayesian Models of Nonstationary Markov Decision Problems.
Nicholas
K. Jong and Peter Stone.
In IJCAI 2005 workshop on Planning
and Learning in A Priori Unknown or Dynamic Domains, August 2005.
Workshop
webpage.
Standard reinforcement learning algorithms generate policies that optimize expected future rewards in a priori unknown domains, but they assume that the domain does not change over time. Prior work cast the reinforcement learning problem as a Bayesian estimation problem, using experience data to condition a probability distribution over domains. In this paper we propose an elaboration of the typical Bayesian model that accounts for the possibility that some aspect of the domain changes spontaneously during learning. We develop a reinforcement learning algorithm based on this model that we expect to react more intelligently to sudden changes in the behavior of the environment.
@inproceedings(IJCAI05ws,
author="Nicholas K.\ Jong and Peter Stone",
title="Bayesian Models of Nonstationary Markov Decision Problems",
booktitle="{IJCAI} 2005 workshop on Planning and Learning in A Priori Unknown or Dynamic Domains",
month="August",year="2005",
abstract={
Standard reinforcement learning algorithms generate
policies that optimize expected future rewards
in a priori unknown domains, but they assume that
the domain does not change over time. Prior work
cast the reinforcement learning problem as a
Bayesian estimation problem, using experience data
to condition a probability distribution over
domains. In this paper we propose an elaboration of
the typical Bayesian model that accounts for the
possibility that some aspect of the domain
changes spontaneously during learning. We develop
a reinforcement learning algorithm based on this
model that we expect to react more intelligently to
sudden changes in the behavior of the environment.
},
wwwnote={<a href="http://www-rcf.usc.edu/~skoenig/workshop.html">Workshop webpage</a>.},
)
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