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cs.GT2024
Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback
Jing Dong, Baoxiang Wang, Yaoliang Yu
We study the problem of no-regret learning algorithms for general monotone and smooth games and their last-iterate convergence properties. Specifically, we investigate the problem…
cs.GT2024
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
Jing Dong, Baoxiang Wang, Yaoliang Yu
In this work, we study potential games and Markov potential games under stochastic cost and bandit feedback. We propose a variant of the Frank-Wolfe algorithm with sufficient explo…