A natural goal in multiagent learning besides finding equilibria is to learn rationalizable behavior, where players learn to avoid iteratively dominated actions. However, even in the basic setting of multiplayer general-sum games, existing algorithms require a number of samples exponential in the number of players to learn rationalizable equilibria under bandit feedback. This paper develops the first line of efficient algorithms for learning rationalizable Coarse Correlated Equilibria (CCE) and Correlated Equilibria (CE) whose sample complexities are polynomial in all problem parameters including the number of players. To achieve this result, we also develop a new efficient algorithm for the simpler task of finding one rationalizable action profile (not necessarily an equilibrium), whose sample complexity substantially improves over the best existing results of Wu et al. (2021). Our algorithms incorporate several novel techniques to guarantee rationalizability and no (swap-)regret simultaneously, including a correlated exploration scheme and adaptive learning rates, which may be of independent interest. We complement our results with a sample complexity lower bound showing the sharpness of our guarantees.

本文提出了第一种高效算法用于学习通过较粗的相关均衡(CCE)和相关均衡(CE)合理化行为的多智能体学习，这些算法的样本复杂度与所有问题参数（包括玩家数量）的多项式成正比，同时还开发了一种新的高效算法来找到一个合理化的行动规划（不一定是均衡），该算法的样本复杂度显著优于现有结果。算法采用了几种新技术来同时保证理性和无择性遗憾，包括相关探索方案和自适应学习速率。

多人游戏中学习可理性均衡