13 papers
First-order methods almost always avoid saddle points: the case of vanishing step-sizes
Ioannis Panageas, Georgios Piliouras, Xiao Wang
In a series of papers \cite{LSJR16, PP17, LPP}, it was established that some of the most commonly used first order methods almost surely (under random initializations) and with ste…
Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games
Stefanos Leonardos, Will Overman, Ioannis Panageas +1
Potential games are arguably one of the most important and widely studied classes of normal form games. They define the archetypal setting of multi-agent coordination as all agent…
A Quadratic Speedup in Finding Nash Equilibria of Quantum Zero-Sum Games
Francisca Vasconcelos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos +2
Recent developments in domains such as non-local games, quantum interactive proofs, and quantum generative adversarial networks have renewed interest in quantum game theory and, sp…
Learning in Quantum Common-Interest Games and the Separability Problem
Wayne Lin, Georgios Piliouras, Ryann Sim +1
Learning in games has emerged as a powerful tool for machine learning with numerous applications. Quantum games model interactions between strategic players who have access to quan…
Passivity, No-Regret, and Convergent Learning in Contractive Games
Hassan Abdelraouf, Georgios Piliouras, Jeff S. Shamma
We investigate the interplay between passivity, no-regret, and convergence in contractive games for various learning dynamic models and their higher-order variants. Our setting is…
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…