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Multi-policy optimization in decentralized autonomic systems

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conference contribution
posted on 2012-07-17, 08:52 authored by Ivana Dusparic, Vinny Cahill
This paper addresses the challenge of multi-policy optimization in decentralized autonomic systems. We evaluate several multi-policy reinforcement learning-based optimization techniques in an urban tra c control simulation, a canonical example of a decentralized autonomic system. Our results indicate that W-learning, which learns separately for each policy and then selects between nominated actions based on current action importance, is a suitable approach for optimization towards multiple policies on non-collaborating agents in heterogeneous autonomic environments.

History

Publication

8th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2009);

Publisher

IFAAMAS

Note

peer-reviewed

Other Funding information

SFI

Language

English

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