Paper:
Language: English:

Adaptive Reinforcement Learning Integrating Exploitation-and Exploration-oriented Learning

Satoshi Kurihara^*, Rikio Onai^** and Toshiharu Sugawara^*

^*NTT Network Innovation Laboratories 3-9-11 Midori-Cho, Musashino-Shi, Tokyo, 180-8585 Japan Tel: +81 422 59 4139, Fax: +81 422 59 2225
^**NTT Software Corporation 209 Yamashita-cho Naka-ku Yokohama-shi, Kanagawa 231-8551 Japan

Received: une 23, 1999

Accepted: August 21, 1999

Keywords: Reinforcement learning, Exploitation-oriented learning, Exploration-oriented learning, Multi-agent model, Dynamic environment

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.3, No.6 pp. 474-478, 1999

Abstract

We propose and evaluate an adaptive reinforcement learning system that integrates both exploitation- and exploration-oriented learning (ArLee). Compared to conventional reinforcement learning, ArLee is more robust in a dynamically changing environment and conducts exploration-oriented learning efficiently even in a large-scale environment. It is thus well suited for autonomous systems, for example, software agents and mobile robots, that operate in dynamic, large-scale environments, such as the real world and the Internet. Simulation demonstrates the learning system\'s basic effectiveness.

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