论文标题
从庞加莱复发到不完美的信息游戏中的融合：通过正规化找到平衡
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization
论文作者
论文摘要
在本文中，我们研究了顺序不完美的信息游戏（IIG）中的正规领导者动态。我们将庞加莱复发的现有结果概括从普通形式游戏到零和两个玩家不完美的信息游戏和其他顺序游戏设置。然后，我们研究游戏的适应奖励是如何在单调游戏中提供强大的融合保证的。我们继续展示如何利用这种奖励适应技术来构建算法，从而完全融合了NASH平衡。最后，我们展示了如何直接使用这些见解来构建针对零和两人播放器不完美的信息游戏（IIG）的最先进的无模型算法。
In this paper we investigate the Follow the Regularized Leader dynamics in sequential imperfect information games (IIG). We generalize existing results of Poincaré recurrence from normal-form games to zero-sum two-player imperfect information games and other sequential game settings. We then investigate how adapting the reward (by adding a regularization term) of the game can give strong convergence guarantees in monotone games. We continue by showing how this reward adaptation technique can be leveraged to build algorithms that converge exactly to the Nash equilibrium. Finally, we show how these insights can be directly used to build state-of-the-art model-free algorithms for zero-sum two-player Imperfect Information Games (IIG).
