Proceedings of the Annual Conference of JSAI
Online ISSN : 2758-7347
33rd (2019)
Session ID : 3P4-J-7-02
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An allocation strategy with deep reinforcement learning for efficient task processing in multi agent system
*Genki MATSUNOSho TANAKAHiroki HARASyunyo KAWAMOTOSyo SHIMOYAMATakashi KAWASHIMADaisuke TSUMITAYasushi KIDOOsamu HASHIMOTOTomohiro TAKAGI
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Abstract

In this study, it was considered how to make matching between task required resource and member capability that will be applied into multi-agent systems. Supported by Reinforcement Learning strategy with deep Neural Network technique, a modern solution was conducted accompanied with standard baseline methods and evaluated from several suggestive viewpoints. According to the numerical experiments, it is elucidated that RL strategy has some advantages when targeting on both execution time duration and accuracy of combination matching.

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© 2019 The Japanese Society for Artificial Intelligence
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