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Proceedings Paper

Effect of reinforcement learning on coordination of multiangent systems
Author(s): Satish T. S. Bukkapatnam; Greg Gao
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Paper Abstract

For effective coordination of distributed environments involving multiagent systems, learning ability of each agent in the environment plays a crucial role. In this paper, we develop a simple group learning method based on reinforcement, and study its effect on coordination through application to a supply chain procurement scenario involving a computer manufacturer. Here, all parties are represented by self-interested, autonomous agents, each capable of performing specific simple tasks. They negotiate with each other to perform complex tasks and thus coordinate supply chain procurement. Reinforcement learning is intended to enable each agent to reach a best negotiable price within a shortest possible time. Our simulations of the application scenario under different learning strategies reveals the positive effects of reinforcement learning on an agent's as well as the system's performance.

Paper Details

Date Published: 29 December 2000
PDF: 11 pages
Proc. SPIE 4208, Network Intelligence: Internet-based Manufacturing, (29 December 2000); doi: 10.1117/12.411778
Show Author Affiliations
Satish T. S. Bukkapatnam, Univ. of Southern California (United States)
Greg Gao, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 4208:
Network Intelligence: Internet-based Manufacturing
Nina M. Berry, Editor(s)

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