论文标题
通过贪婪的遗憾最小化在正常游戏中的平衡发现
Equilibrium Finding in Normal-Form Games Via Greedy Regret Minimization
论文作者
论文摘要
我们通过基于在运行时观察到的遗憾来扩展了经典的遗憾最小化框架，以近似正常游戏中的平衡。从理论上讲，我们的方法保留所有以前的收敛率保证。从经验上讲，AI基准外交的大型随机游戏和正常形式子游戏的实验表明，贪婪的权重在使用采样时要比以前的方法优于先前的方法，有时还要通过几个数量级。
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.
