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Author: Fumito Uwano

Affiliation: Department of Computer Science, Okayama University, 3-1-1, Tsushima-naka, Kita-ku, Okayama, Japan

Keyword(s): Multiagent System, Reinforcement Learning, LSTM, Hetero-information, Hetero-transition.

Abstract: This study focuses on noncommunicative multiagent learning with hetero-information where agents observe each other in different resolutions of information. A new method is proposed for adapting the time dimension of the hetero-information from the observation by expanding the Asynchronous Advantage Actor–Critic (A3C) algorithm. The profit minimizing reinforcement learning with oblivion of memory mechanism was the previously used noncommunicative and cooperative learning method in multiagent reinforcement learning. We then insert an long short-term memory (LSTM) module into the A3C neural network to adapt to the time dimension influence of the hetero-information. The experiments investigate the performance of the proposed method on the hetero-information environment in terms of the effectiveness of LSTM. The experimental results show that: (1) the proposed method performs better than A3C. Without the LSTM module, the proposed method enabled the agents’ learning to converge. (2) LSTM c an adapt the time dimension of the input information. (More)

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Paper citation in several formats:
Uwano, F. (2022). LSTM-based Abstraction of Hetero Observation and Transition in Non-Communicative Multi-Agent Reinforcement Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 172-179. DOI: 10.5220/0010795700003116

@conference{icaart22,
author={Fumito Uwano.},
title={LSTM-based Abstraction of Hetero Observation and Transition in Non-Communicative Multi-Agent Reinforcement Learning},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2022},
pages={172-179},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010795700003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - LSTM-based Abstraction of Hetero Observation and Transition in Non-Communicative Multi-Agent Reinforcement Learning
SN - 978-989-758-547-0
IS - 2184-433X
AU - Uwano, F.
PY - 2022
SP - 172
EP - 179
DO - 10.5220/0010795700003116
PB - SciTePress

- Science and Technology Publications, Lda.
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