Experience-based Reinforcement Learning to Acquire Effective Behavior in a Multiagent Domain

Sachiyo Arai, Katia Sycara, and Terence Payne
The Sixth Pacific Rim International Conference on Artificial Intelligence (PRICAI 2000), 2000, pp. 125-135.


Download
  • Adobe portable document format (pdf) (271KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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",
}