论文标题
一个简单的自适应过程，融合到宽容的相关平衡
A Simple Adaptive Procedure Converging to Forgiving Correlated Equilibria
论文作者
论文摘要
已知与正常形式游戏相关的简单自适应程序（Hart and Mas-Colell 2000）已知存在，但是广泛形式的游戏不存在这样的类似物。利用Zinkevich等人的灵感。 （2008年），我们表明，为正常游戏设计的任何内部遗憾最小化程序都可以有效地扩展到有限的广泛形式的完美召回式游戏。我们的过程将相关平衡解决方案概念的其他各种拟议扩展与广泛形式的游戏融合到一组宽容的相关平衡集合（Forges 1986a; Forges 1986b; Von Stengel and Forges 2008）。在宽容的相关平衡中，玩家仅在游戏开始时就直接达到相关信息时就会收到移动建议。假设所有其他玩家都遵循他们的建议，则每个球员都会激励她的建议，无论她是否在以前的信息中都这样做。最终的程序是完全分散的：玩家既不需要对对手的行为的了解，甚至不需要对游戏本身的完全理解，超出了自己的回报和策略。
Simple adaptive procedures that converge to correlated equilibria are known to exist for normal form games (Hart and Mas-Colell 2000), but no such analogue exists for extensive-form games. Leveraging inspiration from Zinkevich et al. (2008), we show that any internal regret minimization procedure designed for normal-form games can be efficiently extended to finite extensive-form games of perfect recall. Our procedure converges to the set of forgiving correlated equilibria, a refinement of various other proposed extensions of the correlated equilibrium solution concept to extensive-form games (Forges 1986a; Forges 1986b; von Stengel and Forges 2008). In a forgiving correlated equilibrium, players receive move recommendations only upon reaching the relevant information set instead of all at once at the beginning of the game. Assuming all other players follow their recommendations, each player is incentivized to follow her recommendations regardless of whether she has done so at previous infosets. The resulting procedure is completely decentralized: players need neither knowledge of their opponents' actions nor even a complete understanding of the game itself beyond their own payoffs and strategies.
