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Convergence, Targeted Optimality and Safety in Multiagent Learning.
Doran
Chakraborty and Peter Stone.
In Proceedings of the Twenty-seventh
International Conference on Machine Learning (ICML), June 2010.
[PDF]196.9kB [postscript]474.1kB
This paper introduces a novel multiagent learning algorithm which achieves convergence, targeted optimality against memory-bounded adversaries, and safety, in arbitrary repeated games. Called CMLeS, its most novel aspect is the manner in which it guarantees (in a PAC sense) targeted optimality against memory-bounded adversaries, via efficient exploration and exploitation. CMLeS is fully implemented and we present empirical results demonstrating its effectiveness.
@InProceedings{ICML10-chakraborty,
author = "Doran Chakraborty and Peter Stone",
title = "Convergence, Targeted Optimality and Safety in Multiagent Learning",
booktitle = "Proceedings of the Twenty-seventh International Conference on Machine Learning (ICML)",
location = "Haifa, Israel",
month = "June",
year = "2010",
abstract = {
This paper introduces a novel multiagent learning algorithm which
achieves convergence, targeted optimality against memory-bounded
adversaries, and safety, in arbitrary repeated games. Called CMLeS, its
most novel aspect is the manner in which it guarantees (in a PAC sense)
targeted optimality against memory-bounded adversaries, via efficient
exploration and exploitation. CMLeS is fully implemented and we present
empirical results demonstrating its effectiveness.
},
}
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