114 citations · 813 across the 92 of their papers we have counts for
16 papers · 1 filter
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…
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…
No-Regret Learning and Equilibrium Computation in Quantum Games
Wayne Lin, Georgios Piliouras, Ryann Sim +1
As quantum processors advance, the emergence of large-scale decentralized systems involving interacting quantum-enabled agents is on the horizon. Recent research efforts have explo…
Approximating Nash Equilibria in Normal-Form Games via Stochastic Optimization
Ian Gemp, Luke Marris, Georgios Piliouras
We propose the first loss function for approximate Nash equilibria of normal-form games that is amenable to unbiased Monte Carlo estimation. This construction allows us to deploy s…
Beyond Strict Competition: Approximate Convergence of Multi Agent Q-Learning Dynamics
Aamal Hussain, Francesco Belardinelli, Georgios Piliouras
The behaviour of multi-agent learning in competitive settings is often considered under the restrictive assumption of a zero-sum game. Only under this strict requirement is the beh…
Stability of Multi-Agent Learning: Convergence in Network Games with Many Players
Aamal Hussain, Dan Leonte, Francesco Belardinelli +1
The behaviour of multi-agent learning in many player games has been shown to display complex dynamics outside of restrictive examples such as network zero-sum games. In addition, i…
Multiplicative Updates for Online Convex Optimization over Symmetric Cones
Ilayda Canyakmaz, Wayne Lin, Georgios Piliouras +1
We study online convex optimization where the possible actions are trace-one elements in a symmetric cone, generalizing the extensively-studied experts setup and its quantum counte…
Data-Scarce Identification of Game Dynamics via Sum-of-Squares Optimization
Iosif Sakos, Antonios Varvitsiotis, Georgios Piliouras
Understanding how players adjust their strategies in games, based on their experience, is a crucial tool for policymakers. It enables them to forecast the system's eventual behavio…
Chaos persists in large-scale multi-agent learning despite adaptive learning rates
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Konstantinos Metaxas +1
Multi-agent learning is intrinsically harder, more unstable and unpredictable than single agent optimization. For this reason, numerous specialized heuristics and techniques have b…
Equilibrium-Invariant Embedding, Metric Space, and Fundamental Set of Normal-Form Games
Luke Marris, Ian Gemp, Georgios Piliouras
Equilibrium solution concepts of normal-form games, such as Nash equilibria, correlated equilibria, and coarse correlated equilibria, describe the joint strategy profiles from whic…
Generative Adversarial Equilibrium Solvers
Denizalp Goktas, David C. Parkes, Ian Gemp +5
We introduce the use of generative adversarial learning to compute equilibria in general game-theoretic settings, specifically the generalized Nash equilibrium (GNE) in pseudo-game…
A stochastic variant of replicator dynamics in zero-sum games and its invariant measures
Maximilian Engel, Georgios Piliouras
We study the behavior of a stochastic variant of replicator dynamics in two-agent zero-sum games. We characterize the statistics of such systems by their invariant measures which c…
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…
Asymptotic Convergence and Performance of Multi-Agent Q-Learning Dynamics
Aamal Abbas Hussain, Francesco Belardinelli, Georgios Piliouras
Achieving convergence of multiple learning agents in general -player games is imperative for the development of safe and reliable machine learning (ML) algorithms and their appl…
Min-Max Optimization Made Simple: Approximating the Proximal Point Method via Contraction Maps
Volkan Cevher, Georgios Piliouras, Ryann Sim +1
In this paper we present a first-order method that admits near-optimal convergence rates for convex/concave min-max problems while requiring a simple and intuitive analysis. Simila…
Heterogeneous Beliefs and Multi-Population Learning in Network Games
Shuyue Hu, Harold Soh, Georgios Piliouras
The effect of population heterogeneity in multi-agent learning is practically relevant but remains far from being well-understood. Motivated by this, we introduce a model of multi-…