A theory of profit sharing in dynamic environment

Authors:
Shingo Kato;Hiroshi Matsuo
Affiliations:
Department of Electrical & Computer Engineering, Nagoya Institute of Technology, Nagoya, Japan;Department of Electrical & Computer Engineering, Nagoya Institute of Technology, Nagoya, Japan
Venue:
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Year:
2000

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Abstract

Reinforcement learning is one of the most popular learning method for machine learning. Some reinforcement learning algorithms for adapting to the dynamic environment are proposed. In this paper, the number of episode to suppress the ineffective rule after the change of the environment was examined analytically. Afterwards, the forgettable profit sharing method to suppress the ineffective rule quickly is proposed, and the effectiveness was experimentally confirmed comparing the proposed method with conventional method.