Equilibrium Finding in Normal-Form Games Via Greedy Regret Minimization

Hugh Zhang; Adam Lerer; Noam Brown

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arXiv · 2204.04826

Equilibrium Finding in Normal-Form Games Via Greedy Regret Minimization

Hugh Zhang·Adam Lerer·Noam Brown

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Abstract

We extend the classic regret minimization framework for approximating equilibria in normal-form games by greedily weighing iterates based on regrets observed at runtime. Theoretically, our method retains all previous convergence rate guarantees. Empirically, experiments on large randomly generated games and normal-form subgames of the AI benchmark Diplomacy show that greedy weights outperforms previous methods whenever sampling is used, sometimes by several orders of magnitude.

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Hugh Zhang, Adam Lerer, Noam Brown. 2022-04-11. Equilibrium Finding in Normal-Form Games Via Greedy Regret Minimization. https://arxiv.org/abs/2204.04826

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