کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5071599 | 1477067 | 2015 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Near-optimal no-regret algorithms for zero-sum games
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موضوعات مرتبط
علوم انسانی و اجتماعی
اقتصاد، اقتصادسنجی و امور مالی
اقتصاد و اقتصادسنجی
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چکیده انگلیسی
We propose a new no-regret learning algorithm. When used against an adversary, our algorithm achieves average regret that scales optimally as O(1T) with the number T of rounds. However, when our algorithm is used by both players of a zero-sum game, their average regret scales as O(lnâ¡TT), guaranteeing a near-linear rate of convergence to the value of the game. This represents an almost-quadratic improvement on the rate of convergence to the value of a zero-sum game known to be achievable by any no-regret learning algorithm. Moreover, it is essentially optimal as we also show a lower bound of Ω(1T) for all distributed dynamics, as long as the players do not know their payoff matrices in the beginning of the dynamics. (If they did, they could privately compute minimax strategies and play them ad infinitum.)
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Games and Economic Behavior - Volume 92, July 2015, Pages 327-348
Journal: Games and Economic Behavior - Volume 92, July 2015, Pages 327-348
نویسندگان
Constantinos Daskalakis, Alan Deckelbaum, Anthony Kim,