Title: No-Regret Learning in Strongly Monotone Games Converges to a Nash Equilibrium. Abstract: This paper studies a class of online games involving multiple agents with continuous actions that aim to minimize their local loss functions. An open question in the study of online games is whether no-regret learning for such agents leads to a Nash equilibrium. We address this question by providing a sufficient condition for strongly monotone games that guarantees Nash equilibrium convergence in a time average sense. Furthermore, we show that the class of games for which no-regret learning leads to a Nash equilibrium can be expanded if some further information on the learning algorithm is known. Specifically, we provide relaxed sufficient conditions for first-order and zeroth-order gradient descent algorithms as well as for best response algorithms in which agents choose actions that best respond to other players' actions during the last episode. We analyze the convergence rate for these algorithms and present numerical experiments on three economic market problems to illustrate their performance.
