The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

The effectiveness of multirobot learning in achieving optimal, cooperative solutions is potentially affected by various factors having to do with the nature and configuration of the robots and the nature of the learning entities. Varying one factor wrongly may lead to undesirable results. There is no reported work on how systematically to set up these factors. In this paper, we methodically test the effect of varying four common factors (reward scope, global information delay, diversity of robots, and number of robots) in a decentralized multirobot system, first in simulation and then on real robots. The results show that two of these factors, reward scope and global information delay, if set up incorrectly, can prevent optimal, cooperative solutions.

Keywords

Multirobot, Learning, Cooperation

Notes

Number of pages: 6

Text Reference

Poj Tangamchit, John M. Dolan, and Pradeep Khosla, "Crucial Factors Affecting Cooperative Multirobot Learning," IEEE/RSJ International Conference on Intelligent Robots and Systems 2003, October, 2003.

BibTeX Reference

@inproceedings{Tangamchit_2003_4474,
   author = "Poj Tangamchit and John M Dolan and Pradeep Khosla",
   title = "Crucial Factors Affecting Cooperative Multirobot Learning",
   booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems 2003",
   month = "October",
   year = "2003",
}

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