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Authors: Toshihiro Matsui and Hiroshi Matsuo

Affiliation: Nagoya Institute of Technology, Japan

ISBN: 978-989-758-219-6

Keyword(s): Multiagent System, Reinforcement Learning, Distributed Constraint Optimization, Unfairness, Leximin.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Cooperation and Coordination ; Distributed and Mobile Software Systems ; Distributed Problem Solving ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Multi-Agent Systems ; Software Engineering ; Symbolic Systems

Abstract: Reinforcement learning has been studied for cooperative learning and optimization methods in multiagent systems. In several frameworks of multiagent reinforcement learning, the system’s whole problem is decomposed into local problems for agents. To choose an appropriate cooperative action, the agents perform an optimization method that can be performed in a distributed manner. While the conventional goal of the learning is the maximization of the total rewards among agents, in practical resource allocation problems, unfairness among agents is critical. In several recent studies of decentralized optimization methods, unfairness was considered a criterion. We address an action selection method based on leximin criteria, which reduces the unfairness among agents, in decentralized reinforcement learning. We experimentally evaluated the effects and influences of the proposed approach on classes of sensor network problems.

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Paper citation in several formats:
Matsui, T. and Matsuo, H. (2017). A Study on Cooperative Action Selection Considering Unfairness in Decentralized Multiagent Reinforcement Learning.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-219-6, pages 88-95. DOI: 10.5220/0006203800880095

@conference{icaart17,
author={Toshihiro Matsui. and Hiroshi Matsuo.},
title={A Study on Cooperative Action Selection Considering Unfairness in Decentralized Multiagent Reinforcement Learning},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2017},
pages={88-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006203800880095},
isbn={978-989-758-219-6},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A Study on Cooperative Action Selection Considering Unfairness in Decentralized Multiagent Reinforcement Learning
SN - 978-989-758-219-6
AU - Matsui, T.
AU - Matsuo, H.
PY - 2017
SP - 88
EP - 95
DO - 10.5220/0006203800880095

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