NDLI: Profit Sharing That Can Learn Deterministic Policy for POMDPs Environments by Kohonen Feature Map Associative Memory

Content Provider	IEEE Xplore Digital Library
Author	Koma, D. Osana, Y.
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Sci., Tokyo Univ. of Technol., Tokyo, Japan (Koma, D.; Osana, Y.)
Abstract	In this paper, we propose a Profit Sharing that can learn deterministic policy for POMDPs environments by Kohonen Feature Map (KFM) associative memory. The proposed method is based on the conventional Profit Sharing that can learn deterministic policy for POMDPs environments which can obtain the deterministic policy by using the history of observations and the variable-sized Kohonen Feature Map Probabilistic Associative Memory (for short, KFM associative memory). In the conventional Profit Sharing that can learn deterministic policy for POMDPs environments, robustness for noise does not guaranteed. In the proposed method, rules which are obtained in the Profit Sharing that can learn deterministic policy for POMDPs environments are trained by the KFM associative memory and appropriate action selection is realized in noisy environment. We carried out a series of computer experiments, and confirmed that the proposed method can learn rules for deterministic action selection as similar as the conventional Profit Sharing that can learn deterministic policy for POMDPs environments, and appropriate action selection is realized in noisy environment.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	2651
Ending Page	2658
File Size	592261
Page Count	8
File Format	PDF
ISBN	9781479906529
DOI	10.1109/SMC.2013.453
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-13
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Associative memory Vectors Probabilistic logic Educational institutions History Noise measurement
Content Type	Text
Resource Type	Article

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