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Carnegie Mellon Robotics Institute

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Abstract

In this paper, we discuss Profit-sharing, an experience-based reinforcement learning approach (which is similar to a Monte-Carlo based reinforcement learning method) that can be used to learn robust and effective actions within uncertain, dynamic, multi-agent systems. We introduce the cut-loop routine that discards looping behavior, and demonstrate its effectiveness empirically within a simplified NEO (non-combatant evacuation operation) domain. This domain consists of several agents which ferry groups of evacuees to one of several shelters. We demonstrate that the cut-loop routine makes the Profit-sharing approach adaptive and robust within a dynamic and uncertain domain, without the need for predefined knowledge or subgoals. We also compare it empirically with the popular Q-learning approach.

Notes

Associated Center(s) / Consortia: Center for Integrated Manfacturing Decision Systems

Associated Lab(s) / Group(s): Advanced Agent - Robotics Technology Lab

Text Reference

Sachiyo Arai, Katia Sycara, and Terence Payne, "Experience-based Reinforcement Learning to Acquire Effective Behavior in a Multiagent Domain," The Sixth Pacific Rim International Conference on Artificial Intelligence (PRICAI 2000), 2000, pp. 125-135.

BibTeX Reference

@inproceedings{Arai_2000_3729,
   author = "Sachiyo Arai and Katia Sycara and Terence Payne",
   title = "Experience-based Reinforcement Learning to Acquire Effective Behavior in a Multiagent Domain",
   booktitle = "The Sixth Pacific Rim International Conference on Artificial Intelligence (PRICAI 2000)",
   pages = "125-135",
   publisher = "Springer-Verlag",
   year = "2000",
}

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