The Necessity of Average Rewards in Cooperative Multirobot Learning
Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, Vol. 2, pp. 1296 - 1301, May, 2002
Abstract
Learning can be an effective way for robot systems to deal with dynamic environments and changing task conditions. However, popular single-robot learning algorithms based on discounted rewards, such as Q learning, do not achieve cooperation (i.e., purposeful division of labor) when applied to task-level multirobot systems. A task-level system is defined as one performing a mission that is decomposed into subtasks shared among robots. In this paper, we demonstrate the superiority of average-reward-based learning such as the Monte Carlo algorithm for task-level multirobot systems, and suggest an explanation for this superiority.
BibTeX
@conference{Tangamchit-2002-8429,author = {Poj Tangamchit and John M. Dolan and Pradeep Khosla},
title = {The Necessity of Average Rewards in Cooperative Multirobot Learning},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2002},
month = {May},
volume = {2},
pages = {1296 - 1301},
keywords = {Cooperation, Multirobot, Learning},
}
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