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Learning Complementary Multiagent Behaviors: A Case Study

Shivaram Kalyanakrishnan and Peter Stone. Learning Complementary Multiagent Behaviors: A Case Study. In Jacky Baltes, Michail G. Lagoudakis, Tadashi Naruse, and Saeed Shiry Ghidary, editors, RoboCup 2009: Robot Soccer World Cup XIII, pp. 153–165, Springer Verlag, 2010.
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Some simulations referenced in the paper.

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Abstract

As machine learning is applied to increasingly complex tasks, it is likely that the diverse challenges encountered can only be addressed by combining the strengths of different learning algorithms. We examine this aspect of learning through a case study grounded in the robot soccer context. The task we consider is Keepaway, a popular benchmark for multiagent reinforcement learning from the simulation soccer domain. Whereas previous successful results in Keepaway have limited learning to an isolated, infrequent decision that amounts to a turn-taking behavior (passing), we expand the agents' learning capability to include a much more ubiquitous action (moving without the ball, or getting open), such that at any given time, multiple agents are executing learned behaviors simultaneously. We introduce a policy search method for learning ``GetOpen'' to complement the temporal difference learning approach employed for learning ``Pass''. Empirical results indicate that the learned GetOpen policy matches the best hand-coded policy for this task, and outperforms the best policy found when Pass is learned. We demonstrate that Pass and GetOpen can be learned simultaneously to realize tightly-coupled soccer team behavior.

BibTeX Entry

@incollection{LNAI09-kalyanakrishnan-1,
author = "Shivaram Kalyanakrishnan and Peter Stone",
title = "Learning Complementary Multiagent Behaviors: A Case Study",
booktitle = "{R}obo{C}up 2009: Robot Soccer World Cup {XIII}",
editor = "Jacky Baltes and Michail G. Lagoudakis and Tadashi Naruse
and Saeed Shiry Ghidary",
Publisher="Springer Verlag",
year = "2010",
pages="153--165",
abstract = {
As machine learning is applied to increasingly complex tasks, it is
likely that the diverse challenges encountered can only be addressed by
combining the strengths of different learning algorithms. We examine
this aspect of learning through a case study grounded in the robot
soccer context. The task we consider is Keepaway, a popular benchmark
for multiagent reinforcement learning from the simulation soccer
domain. Whereas previous successful results in Keepaway have limited
learning to an isolated, infrequent decision that amounts to a
turn-taking behavior (passing), we expand the agents' learning
capability to include a much more ubiquitous action (moving without
the ball, or getting open), such that at any given time, multiple
agents are executing learned behaviors simultaneously. We introduce a
policy search method for learning ``GetOpen'' to complement the
temporal difference learning approach employed for learning ``Pass''.
Empirical results indicate that the learned GetOpen policy matches the
best hand-coded policy for this task, and outperforms the best policy
found when Pass is learned. We demonstrate that Pass and GetOpen can
be learned simultaneously to realize tightly-coupled soccer team
behavior.
},
}

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