Equilibrium Invariance, Proximality, and Surrogation: Moreau-Smoothed Best-Response Pathways in Stochastic Nonsmooth Games
Zhuoyu Xiao, Uday V. Shanbhag

TL;DR
This paper introduces Moreau-smoothed best-response schemes for nonsmooth, nonconvex stochastic games, providing convergence guarantees and complexity bounds for equilibrium computation in challenging settings.
Contribution
It develops novel Moreau-smoothed BR algorithms for nonsmooth, nonconvex games, extending equilibrium analysis and convergence guarantees beyond smooth cases.
Findings
Linear and sublinear convergence rates established.
Equilibrium invariance under smoothing demonstrated.
Numerical experiments show promising results.
Abstract
Best-response (BR) schemes represent an important avenue for learning equilibria in noncooperative games. However, extant rate guarantees for BR schemes generally necessitate stringent smoothness requirements on player objectives and the availability of suitably defined eigenvalue bounds, significantly limiting the reach of such schemes, and few schemes if any exist for the efficient resolution of a broad class of nonsmooth and nonconvex games with expectation-valued objectives. This motivates our study of Moreau-smoothed BR schemes that allow for nonsmooth objectives. First, we consider a class of nonsmooth and strongly convex games (but potentially non-monotone) under uncertainty. By presenting an equilibrium invariance claim, we present synchronous and asynchronous schemes, equipped with linear and sublinear rate guarantees and associated complexity statements. Second, faced by…
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Game Theory and Applications · Reinforcement Learning in Robotics
