Cooperative behavior control of robot group using stress antibody allotment reward

Sung Geun Kim*, Shinya Taguchi, Su Ill Hong, Hee Hyol Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Lately, development in robotics for utilizing in both industry and home is in much progress. In this research, a group of robots is made to handle relatively complicated tasks. Cooperative action among robots is one of the research areas in robotics that is progressing remarkably well. Reinforcement learning is known as a common approach in robotics for deploying acquisition of action under dynamic environment. However, until recently, reinforcement learning is only applied to one agent problem. In multi-agent environment where plural robots exist, it was difficult to differentiate between learning of achievement of task and learning of performing cooperative action. This paper introduces a method of implementing reinforcement learning to induce cooperation among a group of robots where its task is to transport luggage of various weights to a destination. The general Q-learning method is used as a learning algorithm. Also, the switching of learning mode is proposed for reduction of learning time and learning area. Finally, grid world simulation is carried out to evaluate the proposed methods.

Original languageEnglish
Pages (from-to)16-21
Number of pages6
JournalArtificial Life and Robotics
Issue number1
Publication statusPublished - 2014 Feb


  • Cooperative action
  • Q-learning
  • Reinforcement learning
  • Robots
  • Stress

ASJC Scopus subject areas

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


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