17 citations · 187 across the 55 of their papers we have counts for
9 papers · 1 filter
Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina +1
Policy gradient methods enjoy strong practical performance in numerous tasks in reinforcement learning. Their theoretical understanding in multiagent settings, however, remains lim…
Mediator Interpretation and Faster Learning Algorithms for Linear Correlated Equilibria in General Extensive-Form Games
Brian Hu Zhang, Gabriele Farina, Tuomas Sandholm
A recent paper by Farina & Pipis (2023) established the existence of uncoupled no-linear-swap regret dynamics with polynomial-time iterations in extensive-form games. The equilibri…
Hidden-Role Games: Equilibrium Concepts and Computation
Luca Carminati, Brian Hu Zhang, Gabriele Farina +2
In this paper, we study the class of games known as hidden-role games in which players are assigned privately to teams and are faced with the challenge of recognizing and cooperati…
Polynomial-Time Linear-Swap Regret Minimization in Imperfect-Information Sequential Games
Gabriele Farina, Charilaos Pipis
No-regret learners seek to minimize the difference between the loss they cumulated through the actions they played, and the loss they would have cumulated in hindsight had they con…
Steering No-Regret Learners to a Desired Equilibrium
Brian Hu Zhang, Gabriele Farina, Ioannis Anagnostides +7
A mediator observes no-regret learners playing an extensive-form game repeatedly across rounds. The mediator attempts to steer players toward some desirable predetermined equil…
Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games
Brian Hu Zhang, Gabriele Farina, Ioannis Anagnostides +7
We introduce a new approach for computing optimal equilibria via learning in games. It applies to extensive-form settings with any number of players, including mechanism design, in…
Regret Matching+: (In)Stability and Fast Convergence in Games
Gabriele Farina, Julien Grand-Clément, Christian Kroer +2
Regret Matching+ (RM+) and its variants are important algorithms for solving large-scale games. However, a theoretical understanding of their success in practice is still a mystery…
The Update-Equivalence Framework for Decision-Time Planning
Samuel Sokota, Gabriele Farina, David J. Wu +4
The process of revising (or constructing) a policy at execution time -- known as decision-time planning -- has been key to achieving superhuman performance in perfect-information g…
On the Convergence of No-Regret Learning Dynamics in Time-Varying Games
Ioannis Anagnostides, Ioannis Panageas, Gabriele Farina +1
Most of the literature on learning in games has focused on the restrictive setting where the underlying repeated game does not change over time. Much less is known about the conver…