Implicit Cooperative Learning on Distribution of Received Reward in Multi-Agent System

Fumito Uwano

2023

Abstract

Multi-agent reinforcement learning (MARL) makes agents cooperate with each other by reinforcement learning to achieve collective action. Generally, MARL enables agents to predict the unknown factor of other agents in reward function to achieve obtaining maximize reward cooperatively, then it is important to diminish the complexity of communication or observation between agents to achieve the cooperation, which enable it to real-world problems. By contrast, this paper proposes an implicit cooperative learning (ICL) that have an agent separate three factors of self-agent can increase, another agent can increase, and interactions influence in a reward function approximately, and estimate a reward function for self from only acquired rewards to learn cooperative policy without any communication and observation. The experiments investigate the performance of ICL and the results show that ICL outperforms the state-of-the-art method in two agents cooperation problem.

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Paper Citation


in Harvard Style

Uwano F. (2023). Implicit Cooperative Learning on Distribution of Received Reward in Multi-Agent System. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-623-1, pages 147-153. DOI: 10.5220/0011593500003393


in Bibtex Style

@conference{icaart23,
author={Fumito Uwano},
title={Implicit Cooperative Learning on Distribution of Received Reward in Multi-Agent System},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2023},
pages={147-153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011593500003393},
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 - Implicit Cooperative Learning on Distribution of Received Reward in Multi-Agent System
SN - 978-989-758-623-1
AU - Uwano F.
PY - 2023
SP - 147
EP - 153
DO - 10.5220/0011593500003393