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- W4287865316 abstract "In this paper, we consider multi-agent learning via online gradient descent in a class of games called $lambda$-cocoercive games, a fairly broad class of games that admits many Nash equilibria and that properly includes unconstrained strongly monotone games. We characterize the finite-time last-iterate convergence rate for joint OGD learning on $lambda$-cocoercive games; further, building on this result, we develop a fully adaptive OGD learning algorithm that does not require any knowledge of problem parameter (e.g. cocoercive constant $lambda$) and show, via a novel double-stopping time technique, that this adaptive algorithm achieves same finite-time last-iterate convergence rate as non-adaptive counterpart. Subsequently, we extend OGD learning to the noisy gradient feedback case and establish last-iterate convergence results -- first qualitative almost sure convergence, then quantitative finite-time convergence rates -- all under non-decreasing step-sizes. To our knowledge, we provide the first set of results that fill in several gaps of the existing multi-agent online learning literature, where three aspects -- finite-time convergence rates, non-decreasing step-sizes, and fully adaptive algorithms have been unexplored before." @default.
- W4287865316 created "2022-07-26" @default.
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- W4287865316 date "2020-07-12" @default.
- W4287865316 modified "2023-09-27" @default.
- W4287865316 title "Finite-time last-iterate convergence for multi-agent learning in games" @default.
- W4287865316 hasPublicationYear "2020" @default.
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