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This work introduces Multiplicatively Optimistic Regret Matching (MORM), an uncoupled learning rule for finite general-sum games that combines a potential-based regret-matching argument with multiplicative stability and Hellinger control of strategy movement.
It is proved that on the exact identity-gauge slice of the extended convex lift, $\varepsilon$-stationarity yields $O(\varepsilon)$-suboptimality on compact exact slices, which in turn yields convergence-rate guarantees for nonsmooth policy-search methods.
This paper presents the first polynomial-time algorithm for learning with exact group invariances that applies uniformly to finite and infinite groups, and shows that exact symmetries can be identified from data and exploited for learning in polynomial time.
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