Multiagent learning using a variable learning rate

Abstract

Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents. This creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They… (More)
DOI: 10.1016/S0004-3702(02)00121-2

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@article{Bowling2002MultiagentLU, title={Multiagent learning using a variable learning rate}, author={Michael H. Bowling and Manuela M. Veloso}, journal={Artif. Intell.}, year={2002}, volume={136}, pages={215-250} }