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In the context of multi-player, general-sum games, there is an increasinginterest in solution concepts modeling some form of communication amongplayers, since they can lead to socially better outcomes with respect to Nashequilibria, and may be reached through learning dynamics in a decentralizedfashion. In this paper, we focus on coarse correlated equilibria (CCEs) insequential games. First, we complete the picture on the complexity of findingsocial-welfare-maximizing CCEs by showing that the problem is not in Poly-APXunless P = NP. Furthermore, simple arguments show that CFR - working withbehavioral strategies - may not converge to a CCE. However, we devise a simplevariant (CFR-S) which provably converges to the set of CCEs, but may beempirically inefficient. Thus, we design a variant of the CFR algorithm (calledCFR-Jr) which approaches the set of CCEs with a regret bound sub-linear in thesize of the game, and is shown to be dramatically faster than CFR-S and thestate-of-the-art algorithms to compute CCEs.

Learning to Correlate in Multi-Player General-Sum Sequential Games