A Study Toward Multi-Objective Multiagent Reinforcement Learning Considering Worst Case and Fairness Among Agents

Toshihiro Matsui

2023

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.

Paper Citation

in Harvard Style

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, pages 269-277. DOI: 10.5220/0011687100003393

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Matsui T.
PY - 2023
SP - 269
EP - 277
DO - 10.5220/0011687100003393