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A general criterion and an algorithmic framework for learning in multi-agent systems
2006
Machine Learning
We offer a new formal criterion for agent-centric learning in multi-agent systems, that is, learning that maximizes one's rewards in the presence of other agents who might also be learning (using the same or other learning algorithms). This new criterion takes in as a parameter the class of opponents. We then provide a modular approach for achieving effective agent-centric learning; the approach consists of a number of basic algorithmic building blocks, which can be instantiated and composed
doi:10.1007/s10994-006-9643-2
fatcat:mz4k4iwklnfhxegbrv6prssm3u