Feb 16, 2024 · TL;DR: This paper proposes a framework for online learning algorithms in monotone games, using a "slingshot" strategy to achieve last-iterate ...
A Slingshot Approach to Learning in Monotone Games - DiVA portal
www.diva-portal.org › smash › record
Aug 14, 2023 · In this paper, we address the problem of computing equilibria in monotone games.The traditional Follow the Regularized Leader algorithms fail to ...
This paper proposes a payoff perturbation technique for the Mirror Descent (MD) algorithm. • Existing algorithms typically find an equilibrium in an.
May 26, 2023 · This paper proposes a payoff perturbation technique for the Mirror Descent (MD) algorithm in games where the gradient of the payoff functions is ...
This paper proposes a payoff perturbation tech- nique for the Mirror Descent (MD) algorithm in games where the gradient of the payoff functions is monotone ...
A Slingshot Approach to Learning in Monotone Games.
Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium ... A Slingshot Approach to Learning in Monotone Games · Kenshi Abe, ...
Jul 29, 2024 · In response, we propose Adaptively Perturbed MD (APMD), which adjusts the magnitude of the perturbation by repeatedly updating the slingshot ...
This paper proposes a payoff perturbation technique for the Mirror Descent (MD) algorithm in games where the gradient of the payoff functions is monotone in the ...
A Slingshot Approach to Learning in Monotone Games. In this paper, we address ... Learning in games considers how multiple agents maximize their own rewar...