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Author: Toshihiro Matsui

Affiliation: Nagoya Institute of Technology, Gokiso-cho Showa-ku Nagoya Aichi 466-8555, Japan

Keyword(s): Multiagent System, Multi-Objective, Reinforcement Learning, Cooperative Problem Solving, Fairness, Leximin.

Abstract: Multiagent reinforcement learning has been studied as a fundamental approach to empirically optimize the policies of cooperative/competitive agents. A previous study proposed an extended class of multi-objective reinforcement learning whose objectives correspond to individual agents, and the worst case and fairness among the objectives was considered. However, that work concentrated on the case of joint-state-action space that is handled by a centralized learner performing an offline learning. Toward decentralized solution methods, we investigate the situations including on-line learning where agents individually own their learning tables and selects optimum joint actions by cooperatively combining the decomposed tables with other agents. We experimentally investigate the possibility and influence of the decomposed approach.

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Paper citation in several formats:
Matsui, T. (2023). A Study Toward Multi-Objective Multiagent Reinforcement Learning Considering Worst Case and Fairness Among Agents. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 269-277. DOI: 10.5220/0011687100003393

@conference{icaart23,
author={Toshihiro Matsui.},
title={A Study Toward Multi-Objective Multiagent Reinforcement Learning Considering Worst Case and Fairness Among Agents},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2023},
pages={269-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011687100003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - A Study Toward Multi-Objective Multiagent Reinforcement Learning Considering Worst Case and Fairness Among Agents
SN - 978-989-758-623-1
IS - 2184-433X
AU - Matsui, T.
PY - 2023
SP - 269
EP - 277
DO - 10.5220/0011687100003393
PB - SciTePress

- Science and Technology Publications, Lda.
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