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TPOT-RL Applied to Network Routing.
Peter Stone.
In Proceedings
of the Seventeenth International Conference on Machine Learning, pp. 935–942, 2000.
ICML-2000
[PDF]161.7kB [postscript]395.0kB
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.
@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={<a href="http://www-csli.stanford.edu/icml2k/">ICML-2000</a>},
)
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