抄録
In human’s cooperative behavior, there are two strategies: a passive behavioral strategy based on others’ behaviors and an active behavioral strategy based on the objective-first. However, it is not clear how to acquire a meta-strategy to switch those strategies. The purpose of the proposed study is to create agents with the meta-strategy and to enable complex behavioral choices with a high degree of coordination. In this study, we have experimented by using multi-agent collision avoidance simulations as an example of cooperative tasks. In the experiments, we have used reinforcement learning to obtain an active strategy and a passive strategy by rewarding the interaction with agents facing each other. Furthermore, we have examined and verified the meta-strategy in situations with opponent’s strategy switched.
本文言語 | English |
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論文番号 | 1786 |
ページ(範囲) | 1-14 |
ページ数 | 14 |
ジャーナル | Applied Sciences (Switzerland) |
巻 | 11 |
号 | 4 |
DOI | |
出版ステータス | Published - 2021 2月 2 |
ASJC Scopus subject areas
- 材料科学(全般)
- 器械工学
- 工学(全般)
- プロセス化学およびプロセス工学
- コンピュータ サイエンスの応用
- 流体および伝熱