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JACIII Vol.3 No.6 pp. 474-478
doi: 10.20965/jaciii.1999.p0474
(1999)

Paper:

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
Published:
December 20, 1999
Keywords:
Reinforcement learning, Exploitation-oriented learning, Exploration-oriented learning, Multi-agent model, Dynamic environment
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.

Cite this article as:
Satoshi Kurihara, Rikio Onai, and Toshiharu Sugawara, “Adaptive Reinforcement Learning Integrating Exploitation-and Exploration-oriented Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.3, No.6, pp. 474-478, 1999.
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Last updated on Oct. 22, 2021