Near-Optimal No-Regret Algorithms for Zero-Sum Games

  title={Near-Optimal No-Regret Algorithms for Zero-Sum Games},
  author={Constantinos Daskalakis and Alan Deckelbaum and Anthony Kim},
We propose a new no-regret learning algorithm. When used against an adversary, our algorithm achieves average regret that scales as O (1/√T) with the number T of rounds. This regret bound is optimal but not rare, as there are a multitude of learning algorithms with this regret guarantee. However, when our algorithm is used by both players of a zero-sum game, their average regret scales as O (ln T/T), guaranteeing a near-linear rate of convergence to the value of the game. This represents an… CONTINUE READING
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