A cooperative behavior learning control of multi-robot using trace information |
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Authors: | Tomofumi Ohshita Ji-Sun Shin Michio Miyazaki Hee-Hyol Lee |
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Affiliation: | (1) Waseda University, Tokyo, Japan;(2) Kanto Gakuin University, Yokohama, Japan |
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Abstract: | 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.
This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January
31–February 2, 2008 |
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Keywords: | Multi-agent systems Cooperative behavior Reinforcement learning Stress antibody allotment reward |
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