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Solving Games with Functional Regret Estimation
[article]
2014
arXiv
pre-print
We propose a novel online learning method for minimizing regret in large extensive-form games. The approach learns a function approximator online to estimate the regret for choosing a particular action. A no-regret algorithm uses these estimates in place of the true regrets to define a sequence of policies. We prove the approach sound by providing a bound relating the quality of the function approximation and regret of the algorithm. A corollary being that the method is guaranteed to converge
arXiv:1411.7974v2
fatcat:57tt42ntbbcvdlqt2jsuqdjway