114 citations · 664 across the 92 of their papers we have counts for
39 papers · 1 filter
What preferences can - and cannot - predict in multi-agent online learning
Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos
We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavior of game dynamics, asking in particular to what extent the combinator…
Regret, equilibrium, and learning in games: A guided tour
Panayotis Mertikopoulos
This note aims to serve as an entry point to the literature on learning in games, a topic with significant theoretical appeal and a wide range of applications -- from machine learn…
An empirical study of Fictitious Play for estimating Nash equilibria in first-price auctions with correlated values
Benjamin Heymann, Panayotis Mertikopoulos
This study concerns the computation of the Nash equilibria of first-price auctions with correlated values. Although some equilibrium computation methods exist for auctions with ind…
Avoiding the Worst: The Computational Complexity of Not Worst-Responding
Mete Şeref Ahunbay, Paul W. Goldberg, Edwin Lock +3
Finding, counting, or determining the existence of pure Nash equilibria, where players must play optimally given the others' actions, are known to be computationally intractable pr…
Robust equilibria in continuous games: From strategic to dynamic robustness
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos +1
In this paper, we examine the robustness of Nash equilibria in continuous games, under both strategic and dynamic uncertainty. Starting with the former, we introduce the notion of…
Multi-agent learning under uncertainty: Recurrence vs. concentration
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos +1
In this paper, we examine the convergence landscape of multi-agent learning under uncertainty. Specifically, we analyze two stochastic models of regularized learning in continuous…
Efficient Kernelized Learning in Polyhedral Games Beyond Full-Information: From Colonel Blotto to Congestion Games
Andreas Kontogiannis, Vasilis Pollatos, Gabriele Farina +2
We examine the problem of efficiently learning coarse correlated equilibria (CCE) in polyhedral games, that is, normal-form games with an exponentially large number of actions per…
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…
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…
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…
Nested replicator dynamics, nested logit choice, and similarity-based learning
Panayotis Mertikopoulos, William H. Sandholm
We consider a model of learning and evolution in games whose action sets are endowed with a partition-based similarity structure intended to capture exogenous similarities between…
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…
Exploiting hidden structures in non-convex games for convergence to Nash equilibrium
Iosif Sakos, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Panayotis Mertikopoulos +1
A wide array of modern machine learning applications - from adversarial models to multi-agent reinforcement learning - can be formulated as non-cooperative games whose Nash equilib…
Payoff-based learning with matrix multiplicative weights in quantum games
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos +1
In this paper, we study the problem of learning in quantum games - and other classes of semidefinite games - with scalar, payoff-based feedback. For concreteness, we focus on the w…
The equivalence of dynamic and strategic stability under regularized learning in games
Victor Boone, Panayotis Mertikopoulos
In this paper, we examine the long-run behavior of regularized, no-regret learning in finite games. A well-known result in the field states that the empirical frequencies of no-reg…
Learning in quantum games
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos
In this paper, we introduce a class of learning dynamics for general quantum games, that we call "follow the quantum regularized leader" (FTQL), in reference to the classical "foll…
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 dominated strategies under imitation dynamics
Panayotis Mertikopoulos, Yannick Viossat
The literature on evolutionary game theory suggests that pure strategies that are strictly dominated by other pure strategies always become extinct under imitative game dynamics, b…
Learning in Games with Quantized Payoff Observations
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos
This paper investigates the impact of feedback quantization on multi-agent learning. In particular, we analyze the equilibrium convergence properties of the well-known "follow the…
No-Regret Learning in Games with Noisy Feedback: Faster Rates and Adaptivity via Learning Rate Separation
Yu-Guan Hsieh, Kimon Antonakopoulos, Volkan Cevher +1
We examine the problem of regret minimization when the learner is involved in a continuous game with other optimizing agents: in this case, if all players follow a no-regret algori…
A unified stochastic approximation framework for learning in games
Panayotis Mertikopoulos, Ya-Ping Hsieh, Volkan Cevher
We develop a flexible stochastic approximation framework for analyzing the long-run behavior of learning in games (both continuous and finite). The proposed analysis template incor…
Routing in an Uncertain World: Adaptivity, Efficiency, and Equilibrium
Dong Quan Vu, Kimon Antonakopoulos, Panayotis Mertikopoulos
We consider the traffic assignment problem in nonatomic routing games where the players' cost functions may be subject to random fluctuations (e.g., weather disturbances, perturbat…
A heuristic for estimating Nash equilibria in first-price auctions with correlated values
Benjamin Heymann, Panayotis Mertikopoulos
Our paper concerns the computation of Nash equilibria of first-price auctions with correlated values. While there exist several equilibrium computation methods for auctions with in…