Adaptation to other agent’s behavior using meta-strategy learning by collision avoidance simulation

Kensuke Miyamoto, Norifumi Watanabe, Yoshiyasu Takefuji

研究成果: Article査読

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
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)
11
4
DOI
出版ステータスPublished - 2021 2 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

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