Learning with minimal information in continuous games
针对连续动作集博弈，设计了一种简单的随机学习规则，玩家仅根据自身收益变化更新动作，无需复杂推理，并证明在策略互补博弈、凹博弈和局部序势博弈中收敛到纳什均衡。
While payoff‐based learning models are almost exclusively devised for finite action games, where players can test every action, it is harder to design such learning processes for continuous games. We construct a stochastic learning rule, designed for games with continuous action sets, which requires no sophistication from the players and is simple to implement: players update their actions according to variations in own payoff between current and previous action. We then analyze its behavior in several classes of continuous games and show that convergence to a stable Nash equilibrium is guaranteed in all games with strategic complements as well as in concave games, while convergence to Nash equilibrium occurs in all locally ordinal potential games as soon as Nash equilibria are isolated.