Analysis for adaptability of policy-improving system with a mixture model of bayesian networks to dynamic environments

Authors:
Daisuke Kitakoshi;Hiroyuki Shioya;Ryohei Nakano
Affiliations:
Nagoya Institute of Technology, Nagoya, Japan;Muroran Institute of Technology, Muroran, Japan;Nagoya Institute of Technology, Nagoya, Japan
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

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Abstract

We have proposed an online policy-improving system of reinforcement learning (RL) agents with a mixture model of Bayesian Networks (BNs), and discussed properties of the system. In this paper, two types of mixture models have been applied to the system. A structure of BN in the mixture model is selected based on data collected by agents in an environment, and is regarded as a stochastic knowledge of the environment. This research investigates the adaptability of our system to dynamic environments containing an unexperienced environment, in which an agent does not have the knowledge.