5 citations · 11 across the 9 of their papers we have counts for
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Accelerated regularized learning in finite N-person games
Kyriakos Lotidis, Angeliki Giannou, Panayotis Mertikopoulos +1
Motivated by the success of Nesterov's accelerated gradient algorithm for convex minimization problems, we examine whether it is possible to achieve similar performance gains in th…
On the convergence of policy gradient methods to Nash equilibria in general stochastic games
Angeliki Giannou, Kyriakos Lotidis, Panayotis Mertikopoulos +1
Learning in stochastic games is a notoriously difficult problem because, in addition to each other's strategic decisions, the players must also contend with the fact that the game…
Survival of the strictest: Stable and unstable equilibria under regularized learning with partial information
Angeliki Giannou, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos
In this paper, we examine the Nash equilibrium convergence properties of no-regret learning in general N-player games. For concreteness, we focus on the archetypal follow the regul…