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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Proposal of Exploitation-Oriented Learning PS-r#
verfasst von : Kazuteru Miyazaki, Shigenobu Kobayashi
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2008
Verlag: Springer Berlin Heidelberg
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Exploitation-oriented Learning
(XoL) is a novel approach to goal-directed learning from interaction. Though
reinforcement learning
is much more focus on the learning and can gurantee the optimality in
Markov Decision Processes
(MDPs) environments, XoL aims to learn
a rational policy
, whose expected reward per an action is larger than zero, very quickly. We know PS-r* that is one of the XoL methods. It can learn
an useful rational policy
that is not inferior to a random walk in
Partially Observed Markov Decision Processes
(POMDPs) environments where the number of types of a reward is one. However, PS-r* requires
O
(
MN
2
) memories where
N
and
M
are the numbers of types of a sensory input and an action.In this paper, we propose PS-r
#
that can learn an useful rational policy in the POMDPs environments by
O
(
MN
) memories. We confirm the effectiveness of PS-r
#
in numerical examples.