Aliased States Discerning in POMDPs and Improved Anticipatory Classifier System | Zendy

Tomohiro Hayashida | Zendy; Ichiro Nishizaki | Zendy; Ryosuke Sakato | Zendy

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Aliased States Discerning in POMDPs and Improved Anticipatory Classifier System

Author(s) -

Tomohiro Hayashida,

Ichiro Nishizaki,

Ryosuke Sakato

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.08.082

Subject(s) - partially observable markov decision process , computer science , classifier (uml) , markov decision process , artificial intelligence , machine learning , benchmark (surveying) , decision process , markov process , markov model , markov chain , statistics , mathematics , process management , geodesy , business , geography

This paper improves a classifier system, ACS (Anticipatory Classifier System). The suggested classifier system is named ACSM (ACS with Memory) which consists of a method of discerning the aliased states in a POMDP (Partially Observable Markov Decision Process), and choosing the proper action based on the internal memory and the sensory information around the agent. A POMDP is one of Markov decision process such that an agent observes local information about the environment. This paper executes some numerical experiments using eight kinds of maze problems which are well used as benchmark problems for POMDPs. ACSM achieves greater experimental result than the existing classifier systems for the maze problems

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