Spotlight Poster

Regret Matching+: (In)Stability and Fast Convergence in Games

Gabriele Farina · Julien Grand-ClĂ©ment · Christian Kroer · Chung-Wei Lee · Haipeng Luo

Great Hall & Hall B1+B2 (level 1) #1708
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Thu 14 Dec 8:45 a.m. PST — 10:45 a.m. PST

Abstract: Regret Matching$^+$ (RM$^+$) and its variants are important algorithms for solving large-scale games.However, a theoretical understanding of their success in practice is still a mystery.Moreover, recent advances on fast convergence in games are limited to no-regret algorithms such as online mirror descent, which satisfy stability.In this paper, we first give counterexamples showing that RM+ and its predictive version can be unstable, which might cause other players to suffer large regret. We then provide two fixes: restarting and chopping off the positive orthant that RM$^+$ works in.We show that these fixes are sufficient to get $O(T^{1/4})$ individual regret and $O(1)$ social regret in normal-form games via RM$^+$ with predictions.We also apply our stabilizing techniques to clairvoyant updates in the uncoupled learning setting for RM$^+$ and prove desirable results akin to recent works for Clairvoyant online mirror descent. Our experiments show the advantages of our algorithms over vanilla RM$^+$-based algorithms in matrix and extensive-form games.

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