Learning Rationalizable Equilibria in Multiplayer Games. (arXiv:2210.11402v1 [cs.LG])

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 …

arxiv equilibria games