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Learning payoff functions in infinite games
2007
Machine Learning
We consider a class of games with real-valued strategies and payoff information available only in the form of data from a given sample of strategy profiles. Solving such games with respect to the underlying strategy space requires generalizing from the data to a complete payoff-function representation. We address payoff-function learning as a standard regression problem, with provision for capturing known structure (e.g., symmetry) in the multiagent environment. To measure learning performance,
doi:10.1007/s10994-007-0715-8
fatcat:pzarp7vtw5cxrbsmiilwazowci