114 citations · 813 across the 92 of their papers we have counts for
61 papers · 1 filter
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…
Prediction Accuracy of Learning in Games : Follow-the-Regularized-Leader meets Heisenberg
Yi Feng, Georgios Piliouras, Xiao Wang
We investigate the accuracy of prediction in deterministic learning dynamics of zero-sum games with random initializations, specifically focusing on observer uncertainty and its re…
On the Stability of Learning in Network Games with Many Players
Aamal Hussain, Dan Leonte, Francesco Belardinelli +1
Multi-agent learning algorithms have been shown to display complex, unstable behaviours in a wide array of games. In fact, previous works indicate that convergent behaviours are le…
MEV Sharing with Dynamic Extraction Rates
Pedro Braga, Georgios Chionas, Piotr Krysta +3
Maximal Extractable Value (MEV) has emerged as a new frontier in the design of blockchain systems. In this paper, we propose making the MEV extraction rate as part of the protocol…
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…
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…
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…
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…
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…
Optimality Despite Chaos in Fee Markets
Stefanos Leonardos, Daniël Reijsbergen, Barnabé Monnot +1
Transaction fee markets are essential components of blockchain economies, as they resolve the inherent scarcity in the number of transactions that can be added to each block. In ea…
Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence
Rahul Jain, Georgios Piliouras, Ryann Sim
Recent advances in quantum computing and in particular, the introduction of quantum GANs, have led to increased interest in quantum zero-sum game theory, extending the scope of lea…
Learning Correlated Equilibria in Mean-Field Games
Paul Muller, Romuald Elie, Mark Rowland +7
The designs of many large-scale systems today, from traffic routing environments to smart grids, rely on game-theoretic equilibrium concepts. However, as the size of an -player…
Fast Convergence of Optimistic Gradient Ascent in Network Zero-Sum Extensive Form Games
Georgios Piliouras, Lillian Ratliff, Ryann Sim +1
The study of learning in games has thus far focused primarily on normal form games. In contrast, our understanding of learning in extensive form games (EFGs) and particularly in EF…
Alternating Mirror Descent for Constrained Min-Max Games
Andre Wibisono, Molei Tao, Georgios Piliouras
In this paper we study two-player bilinear zero-sum games with constrained strategy spaces. An instance of natural occurrences of such constraints is when mixed strategies are used…
Nash, Conley, and Computation: Impossibility and Incompleteness in Game Dynamics
Jason Milionis, Christos Papadimitriou, Georgios Piliouras +1
Under what conditions do the behaviors of players, who play a game repeatedly, converge to a Nash equilibrium? If one assumes that the players' behavior is a discrete-time or conti…
No-Regret Learning in Games is Turing Complete
Gabriel P. Andrade, Rafael Frongillo, Georgios Piliouras
Games are natural models for multi-agent machine learning settings, such as generative adversarial networks (GANs). The desirable outcomes from algorithmic interactions in these ga…
Unpredictable dynamics in congestion games: memory loss can prevent chaos
Jakub Bielawski, Thiparat Chotibut, Fryderyk Falniowski +2
We study the dynamics of simple congestion games with two resources where a continuum of agents behaves according to a version of Experience-Weighted Attraction (EWA) algorithm. Th…
Online Learning in Periodic Zero-Sum Games
Tanner Fiez, Ryann Sim, Stratis Skoulakis +2
A seminal result in game theory is von Neumann's minmax theorem, which states that zero-sum games admit an essentially unique equilibrium solution. Classical learning results build…