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Gal A. Kaminka, Dan Erusalimchik,
and Sarit Kraus. Adaptive Multi-Robot Coordination: A New Perspective.
In Proceedings of the AAMAS 2009 workshop on Adaptive and Learning Agents (ALA), 2009.
A slightly different
version of this paper also appears in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA-2010)
(unavailable)
Multi-robot systems researchers have been investigating adaptive coordination methods for improving spatial coordination in teams. Such methods adapt the coordination method to the dynamic changes in density of the robots. Unfortunately, while their empirical success is evident, none of these methods has been understood in the context of existing formal work on multi-robot learning. This paper presents a reinforcement-learning approach to coordination algorithm selection, which is not only shown to work well in experiments, but is also analytically grounded. We present a reward function (Effectiveness Index, EI), that reduces time and resources spent coordinating, and maximizes the time between conflicts that require coordination. It does this by measuring the resource-spending velocity. We empirically show its success in several domains, including robots in virtual worlds, simulated robots, and physical AIBO robots executing foraging. In addition, we analytically explore the reasons that EI works well. We show that under some assumptions, spatial coordination opportunities can be modeled as matrix games in which the payoffs are directly a function of EI estimates. The use of reinforcement learning leads to robots maximizing their EI rewards in equilibrium. This work is a step towards bridging the gap between the theoretical study of interactions, and their use in multi-robot coordination.
@InProceedings{ala09,
author = {Gal A. Kaminka and Dan Erusalimchik and Sarit Kraus},
title = {Adaptive Multi-Robot Coordination: A New Perspective},
booktitle = {Proceedings of the {AAMAS} 2009 workshop on Adaptive and Learning Agents ({ALA})},
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year = {2009},
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abstract = {Multi-robot systems researchers have been investigating adaptive coordination methods for improving spatial coordination in teams. Such methods adapt the coordination method to the dynamic changes in density of the robots. Unfortunately, while their empirical success is evident, none of these methods has been understood in the context of existing formal work on multi-robot learning.
This paper presents a reinforcement-learning approach to coordination algorithm selection, which
is not only shown to work well in experiments, but is also analytically grounded. We present a reward function (\emph{Effectiveness Index}, EI), that reduces time and resources spent coordinating, and maximizes the time between conflicts that require coordination. It does this by measuring \emph{the resource-spending velocity}. We empirically show its success in several domains, including robots in virtual worlds, simulated robots, and physical AIBO robots executing foraging. In addition, we analytically explore the reasons that EI works well. We show that under some assumptions,
spatial coordination opportunities can be modeled as matrix games in which the payoffs are directly a function of EI estimates. The use of reinforcement learning leads to robots maximizing their EI rewards in equilibrium.
This work is a step towards bridging the gap between the theoretical study of interactions, and their use in multi-robot coordination. },
wwwnote = {A slightly different version of this paper also appears in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA-2010)},
}
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