Regret Testing: Learning to Play Nash Equilibrium Without Knowing You Have an Opponent
提出一类简单且完全解耦的学习规则，玩家无需知道对手的收益或行动，就能在有限两人博弈中逐步逼近纳什均衡行为。
A learning rule is uncoupled if a player does not condition his strategy on the opponent's payoffs. It is radically uncoupled if a player does not condition his strategy on the opponent's actions or payoffs. We demonstrate a family of simple, radically uncoupled learning rules whose period-by-period behavior comes arbitrarily close to Nash equilibrium behavior in any finite two-person game.