In the former, the mediator draws and recommends a complete normal-form plan to each player before the game starts. This is beneficial when the mediator wants to maximize, e.g., the Appendix A.1 provides a suitable formal definition of the set of EFCEs via the notion of trigger agent (originally This holds for arbitrary EFGs with multiple players and/or chance moves. Unfortunately, that algorithm is mainly a theoretical tool, and it is known to have limited scalability beyond toy problems. However, their algorithm is centralized and based on MCMC sampling which may limit its practical appeal. B.1 Proofs for Section 4 The following auxiliary result is exploited in the proof of Theorem 1. Lemma 4. This concludes the proof.Theorem 1.

Recently, there has been growing interest around less-restrictive solution concepts than Nash equilibrium in extensive-form games, with significant effort towards the computation of extensive-form correlated equilibrium (EFCE) and extensive-form coarse correlated equilibrium (EFCCE). In this paper, we show how to leverage the popular counterfactual regret minimization (CFR) paradigm to induce simple no-regret dynamics that converge to the set of EFCEs and EFCCEs in an n-player general-sum extensive-form games. For EFCE, we define a notion of internal regret suitable for extensive-form games and exhibit an efficient no-internal-regret algorithm. These results complement those for normal-form games introduced in the seminal paper by Hart and Mas-Colell. For EFCCE, we show that no modification of CFR is needed, and that in fact the empirical frequency of play generated when all the players use the original CFR algorithm converges to the set of EFCCEs.