A cooperative behavior learning control of multi-robot using trace information

Tomofumi Ohshita, Ji Sun Shin*, Michio Miyazaki, Hee Hyol Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


The distributed autonomous robotic system has superiority of robustness and adaptability to dynamical environment, however, the system requires the cooperative behavior mutually for optimality of the system. The acquisition of action by reinforcement learning is known as one of the approaches when the multi-robot works with cooperation mutually for a complex task. This paper deals with the transporting problem of the multi-robot using Q-learning algorithm in the reinforcement learning. When a robot carries luggage, we regard it as that the robot leaves a trace to the own migrational path, which trace has feature of volatility, and then, the other robot can use the trace information to help the robot, which carries luggage. To solve these problems on multi-agent reinforcement learning, the learning control method using stress antibody allotment reward is used. Moreover, we propose the trace information of the robot to urge cooperative behavior of the multi-robot to carry luggage to a destination in this paper. The effectiveness of the proposed method is shown by simulation.

Original languageEnglish
Pages (from-to)144-147
Number of pages4
JournalArtificial Life and Robotics
Issue number1
Publication statusPublished - 2008 Dec 1


  • Cooperative behavior
  • Multi-agent systems
  • Reinforcement learning
  • Stress antibody allotment reward

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence


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