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

Kensuke Miyamoto, Norifumi Watanabe, Yoshiyasu Takefuji

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Article number1786
Pages (from-to)1-14
Number of pages14
JournalApplied Sciences (Switzerland)
Issue number4
Publication statusPublished - 2021 Feb 2


  • Agent simulation
  • Collision avoidance
  • Cooperative action
  • Meta-strategy
  • Reinforcement learning

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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