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@COMMENT written by Patrick Riley
@COMMENT This file came from Peter Stone's publication pages at
@COMMENT http://www.cs.utexas.edu/~pstone/papers
@InProceedings(ICML2000,
Author="Peter Stone",
Title="{TPOT-RL} Applied to Network Routing",
BookTitle="Proceedings of the Seventeenth International Conference on Machine Learning",
year="2000",
pages="935--942",
abstract={
Team-partitioned, opaque-transition reinforcement
learning (TPOT-RL) is a distributed reinforcement
learning technique that allows a team of independent
agents to learn a collaborative task. TPOT-RL was
first successfully applied to simulated robotic
soccer. This paper demonstrates that TPOT-RL is
general enough to apply to a completely different
domain, namely network packet routing. Empirical
results in an abstract network routing simulator
indicate that agents situated at individual nodes
can learn to efficiently route packets through a
network that exhibits changing traffic patterns,
based on locally observable sensations.
},
wwwnote={ICML-2000},
)