Aliased States Discerning in POMDPs and Improved Anticipatory Classifier System

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.