Tight last-iterate convergence rates for no-regret learning in multi-player games
This addresses a theoretical question in multi-agent learning, providing tight bounds for convergence rates, but it is incremental as it builds on prior work on no-regret algorithms.
The paper tackles the problem of obtaining last-iterate convergence rates for no-regret learning in multi-player games, showing that the optimistic gradient algorithm achieves a tight O(1/√T) rate for smooth monotone games.
We study the question of obtaining last-iterate convergence rates for no-regret learning algorithms in multi-player games. We show that the optimistic gradient (OG) algorithm with a constant step-size, which is no-regret, achieves a last-iterate rate of $O(1/\sqrt{T})$ with respect to the gap function in smooth monotone games. This result addresses a question of Mertikopoulos & Zhou (2018), who asked whether extra-gradient approaches (such as OG) can be applied to achieve improved guarantees in the multi-agent learning setting. The proof of our upper bound uses a new technique centered around an adaptive choice of potential function at each iteration. We also show that the $O(1/\sqrt{T})$ rate is tight for all $p$-SCLI algorithms, which includes OG as a special case. As a byproduct of our lower bound analysis we additionally present a proof of a conjecture of Arjevani et al. (2015) which is more direct than previous approaches.