4 papers · 1 filter
The impact of uncertainty on regularized learning in games
Pierre-Louis Cauvin, Davide Legacci, Panayotis Mertikopoulos
In this paper, we investigate how randomness and uncertainty influence learning in games. Specifically, we examine a perturbed variant of the dynamics of "follow-the-regularized-le…
Characterizing the Convergence of Game Dynamics via Potentialness
Martin Bichler, Davide Legacci, Panayotis Mertikopoulos +2
Understanding the convergence landscape of multi-agent learning is a fundamental problem of great practical relevance in many applications of artificial intelligence and machine le…
No-regret learning in harmonic games: Extrapolation in the face of conflicting interests
Davide Legacci, Panayotis Mertikopoulos, Christos H. Papadimitriou +2
The long-run behavior of multi-agent learning - and, in particular, no-regret learning - is relatively well-understood in potential games, where players have aligned interests. By…
A geometric decomposition of finite games: Convergence vs. recurrence under exponential weights
Davide Legacci, Panayotis Mertikopoulos, Bary Pradelski
In view of the complexity of the dynamics of learning in games, we seek to decompose a game into simpler components where the dynamics' long-run behavior is well understood. A natu…