Nash Equilibrium Computation

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12 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Learning in Continuous Games from Pairwise Preference Feedback
Oct 4, 2026Anas BarakatPairwise Preference Learning Nash Equilibrium Computation
Singapore University of Technology and Design
Fully Online Decentralized Learning in Stochastic Games with Unknown Independent Chains
Oct 1, 2026S. Rasoul EtesamiGame Theory Online Mirror Descent
Department of Industrial and Systems Engineering, Coordinated Science Laboratory University of Illinois Urbana-Champaign, Urbana, IL, USA 61801
Robust Nash Alignment under Preference Uncertainty
Sep 30, 2026Shihab Ahmed, Debamita Ghosh, David Tang +3LLM Alignment Preference Alignment
University of Central Florida · Arizona State University · University of Colorado Boulder
Average-and Last-Iterate Lower Bounds for Optimistic Matrix Mirror-Prox in Quantum Zero-Sum Games
Sep 30, 2026Yiheng Su, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Pucheng XiongLast-Iterate Convergence Oracle Complexity
Department of Computer Sciences, University of Wisconsin-Madison
Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$ -Potential Game Framework
Sep 30, 2026Anran Hu, Zhexin Wang, Yufei Zhang +1Game Theory Autonomous Driving
Department of Industrial Engineering and Operations Research, Columbia University · Department of Mathematics, Imperial College London, London, UK · Department of Civil Engineering and Engineering Mechanics, Data Science Institute, Columbia University
Minimax Last-Iterate Convergence in Matrix Games with Observed Actions
Sep 28, 2026Yuheng ZhangLast-Iterate Convergence Zero-Sum Games
University of Illinois Urbana-Champaign
Dynamical low-rank equilibrium computation for stochastic games between advanced persistent threats and moving target defense
Sep 20, 2026Tian Zijian, Zhang He, Chen Xinjie +2Nash Equilibrium Computation Zero-Sum Games
College of Control Science and Engineering, Zhejiang University, PC 310027, China · Alibaba Group, Hangzhou, China
Efficient Nash Equilibrium Computation for Cybersecurity Games
Sep 16, 2026Michael Lanier, David Farmer, Yevgeniy VorobeychikGame Theory Nash Equilibrium Computation
Washington University in St. Louis
The fixed-point bundle method over product-of-simplex domains arising from game equilibria
Sep 14, 2026Hongbo SunOptimization Convergence Analysis Nash Equilibrium Computation
Hongbo Sun∗
High-Probability Nash Regret for Decentralized Learning in Markov $α$ -Potential Games: Episodic and Fully Online Asynchronous Algorithms with Applications to Markov Congestion Games
Sep 14, 2026S. Rasoul EtesamiPolicy Gradient Asynchronous RL
Department of Industrial and Systems Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign, Urbana, IL, USA
Inference-Time Nash Alignment
Sep 8, 2026Hadi Hosseini, Debmalya Mandal, Duohan ZhangInference-Time Optimization Preference Alignment
Penn State University, USA · University of Warwick, UK
Input-to-State Stability Framework for Fully Distributed Primal-Dual Dynamics for Quadratic GNEPs Without Multiplier Consensus
Sep 7, 2026Shao-An YinPrimal-Dual Optimization Dynamical Systems
independent researcher
Robust PAC Learning of Concurrent Stochastic Games
Sep 3, 2026Angel Y. He, David ParkerGame Theory Robust RL
Department of Engineering Science University of Oxford · Department of Computer Science University of Oxford
PokaiTrainer: Scaling Equilibrium Search to Competitive Pokémon VGC
Aug 29, 2026Max YuGame-Playing Agents Self-Play RL
Independent Researcher
Algorithms for Equilibria in Concurrent Stopping Games
Jul 27, 2026Léonard Brice, Thomas A. Henzinger, K. S. ThejaswiniNash Equilibrium Game Theory
Institute of Science & Technology Austria · Université Libre de Bruxelles
Beyond Bayesian Nash: Learning Minimax-Regret Equilibria for Adversarial Team Games under Asymmetric Information
Jul 10, 2026Naman Aggarwal, Jonathan P. HowImperfect-Information Games Game Theory
Aerospace Control Laboratory Laboratory of Information and Decision Systems Massachusetts Institute of Technology
Offline Nash Solvers Meet Online Tree Search in Multi-Agent Games on Graphs
Jul 9, 2026Mukesh Kumar, Yue Guan, Panagiotis TsiotrasNash Equilibrium Tree Search
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA
Projected Exploitability Descent for Nash Equilibrium Computation in Multiplayer Imperfect-Information Games
Jun 28, 2026Sam GanzfriedImperfect-Information Games Gradient Descent
Ganzfried Research, Cornell University
A Fast Convergent Algorithm for Solving Non-convex Partially-Decoupled Generalized Nash Equilibrium Problems
Jun 26, 2026Bennet Outland, Vishala AryaGame Theory Nonconvex Optimization
Smead Aerospace Engineering Sciences Department, 3775 Discovery Drive, Boulder, CO, USA
Which Nash Equilibrium? Solver-Dependent Selection on Zero-Sum Nash Polytopes
Jun 26, 2026Luis LealNash Equilibrium Imperfect-Information Games
Variable Bound Tightening for Nash Equilibrium Computation in Multiplayer Imperfect-Information Games
Jun 24, 2026Sam GanzfriedNash Equilibrium Branch and Bound
Ganzfried Research, Cornell University
GARIP: A Running-Average Moving Reference for Last-Iterate Self-Play in Two-Player Zero-Sum Games
Jun 21, 2026Can SavcıNash Equilibrium Last-Iterate Convergence
Independent Researcher İzmir, Türkiye
Intermittent Strategic Cooperation of Two Selfish Agents on Graphs
Jun 15, 2026Itay Shedlezki, Noa AgmonNash Equilibrium Game Theory
Bar-Ilan University · Ramat Gan, Israel
DNQ: Deep Nash Q-Network for Partially Observable n-Player Games
Jun 4, 2026Qintong Xie, Edward Koh, Xavier Cadet +1Nash Equilibrium Reinforcement Learning
Fairness in two-player zero-sum games with bandit feedback
May 31, 2026S Akash, Pratik GajaneNash Equilibrium Zero-Sum Games
LatentForce.ai Bengaluru, India · Laboratoire d’Informatique Fondamentale d’Orléans University of Orléans, France
On the Geometry of Games and their Solvers
May 28, 2026Yaqi Sun, Julian Ma, David MguniAlgorithm Selection Game Theory
Queen Mary University of London · University College London
Global Policy-Space Response Oracles for Two-Player Zero-Sum Games
May 27, 2026Junyu Zhang, Feihong Yang, Jian Wang +2Nash Equilibrium Game Theory
Department of Electronic Engineering, Tsinghua University, Beijing, China · Qiyuan Lab, Beijing, China
Real-Time Parallel Counterfactual Regret Minimization
May 19, 2026Boning Li, Longbo HuangGame-Playing Agents GPU Acceleration
IIIS, Tsinghua University
Equilibrium Selection in Multi-Agent Policy Gradients via Opponent-Aware Basin Entry
May 18, 2026Yevhen Shcherbinin, Arina Redina, Maxim Kalpin +1Nash Equilibrium Multi-Agent Coordination
Bloomsbury Technology, London, United Kingdom · London School of Economics and Political Science, London, United Kingdom · University of Bristol, Bristol, United Kingdom +2
Data-Augmented Game Starts for Accelerating Self-Play Exploration in Imperfect Information Games
May 14, 2026JB Lanier, Nathan Monette, Pierre Baldi +1RL Benchmarks Self-Play RL
University of California Irvine · University of Oxford

Oct 4, 2026, cs.GTJ/K move · Enter open · S save

Learning in Continuous Games from Pairwise Preference Feedback

Anas Barakat

Singapore University of Technology and Design

Pairwise Preference Learning Nash Equilibrium Computation

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We study learning in continuous games when players receive only pairwise preference feedback, revealing which of two actions is preferred but neither payoff values nor preference magnitudes. We first show that standard external regret and coarse correlated equilibria (CCE) are not identifiable from this ordinal information: the same sequence of play can incur zero and linear regret in two ordinally equivalent games, while the distributions that remain CCE across all cardinal representations consistent with the same preferences are exactly those supported on pure Nash equilibria. Motivated by this gap, we develop a first-order ordinal theory based on normalized unilateral preference directions, introducing an ordinal directional regret benchmark and corresponding equilibrium notions. We show that block-normalized pseudogradient dynamics achieve sublinear ordinal regret and, under additional structure, Nash-convergence guarantees. We then use a single-comparison estimator to implement these dynamics from finite pairwise comparisons. With one comparison per player and round, the resulting algorithm achieves sublinear finite-resolution ordinal regret against arbitrary opponent behavior and, in ordinal potential games, almost-sure last-iterate convergence to the Nash set. Our results provide regret, dynamics, and equilibrium guarantees directly from preference feedback without reconstructing cardinal utilities.

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