Zero-Sum Games

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5 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 34

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Feedback Dominance Analysis for Pursuit-Evasion Games on Graphs
Oct 5, 2026Yue Guan, Daigo Shishika, Dipankar Maity +2Game Theory Zero-Sum Games
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA. · College of Engineering and Computing, George Mason University, Fairfax, VA, USA. · Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC, USA. +1
Fast Last-Iterate Convergence in Zero-Sum Markov Games with Bandit Feedback
Oct 5, 2026Yuheng ZhangLast-Iterate Convergence Reinforcement Learning
University of Illinois Urbana-Champaign
Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coverage
Oct 4, 2026Shuze Daniel Liu, Claire Chen, Jiuqi Wang +1Imperfect-Information Games Zero-Sum Games
Massachusetts Institute of Technology · Purdue University · California Institute of Technology +1
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
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
Steering Equilibrium Selection in Regularized Self-Play via the Reference Policy
Sep 17, 2026Luis LealNash Equilibrium Self-Play RL
NashDreamer: Model-Based Reinforcement Learning for Zero-Sum Imperfect-Information Games
Sep 1, 2026Tomáš Holeček, Viliam LisýImperfect-Information Games Zero-Sum Games
Artificial Intelligence Center, Department of Computer Science Faculty of Electrical Engineering, Czech Technical University in Prague
Distributed Team Orchestration via Supervisor Networks: Convergence, Optimality, and Resilience
Aug 10, 2026Juntian Zhu, Guanpu Chen, Tongtian Zhu +3Multi-Agent Orchestration Zero-Sum Games
School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, China · School of Automation, Southeast University, Nanjing 210096, China · College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China +2
Beyond the Black Box: Interpretable Models of Human Randomisation Failures
Aug 7, 2026Ngoc Linh DaoInterpretable ML Zero-Sum Games
Alpen-Adria-University of Klagenfurt, Austria
Agents That Certify Their Own Exploits: Confidence-Scheduled Restricted Responses for Safe Opponent Exploitation
Jul 30, 2026Boning Li, Longbo HuangImperfect-Information Games Zero-Sum Games
IIIS, Tsinghua University
The Curvature Shadow: An Apparent Failure of Maximum-Entropy Equilibrium Selection is a Removable Artifact
Jul 20, 2026Luis LealNash Equilibrium Imperfect-Information Games
FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games
Jul 7, 2026Chase McDonald, Nathan Tsang, Wesley N. KerrRL Benchmarks Game-Playing Agents
Como Research · Riot Games
Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games
Jul 1, 2026Kevin Wang, Kevin Yang, Arjun Prakash +1Representation Learning Imperfect-Information Games
Brown University
Entropy Regularized Reinforcement Learning for Zero-Sum Stochastic Differential Games in a Regime-Switching Jump-Diffusion Process
Jun 27, 2026Congde Hu, Zhuo Jin, Danping Li +1Zero-Sum Games Maximum Entropy RL
School of Mathematics and Statistics, Anhui Normal University, Wuhu, 241002, Anhui, China. · Department of Actuarial Studies and Business Analytics, Macquarie University, Sydney, 2109, NSW, Australia. · School of Statistics, KLATASDS-MOE, China Inclusive Ageing Finance Research Center, East China Normal University, Shanghai, 200062, Shanghai, China.
Which Nash Equilibrium? Solver-Dependent Selection on Zero-Sum Nash Polytopes
Jun 26, 2026Luis LealNash Equilibrium Imperfect-Information Games
EMAgnet: Parameter-Space EMA Regularization for Policy Gradient Self-Play in Large Games
Jun 22, 2026Tristan Maidment, JB Lanier, Chase McDonald +5Self-Play RL Imperfect-Information Games
1Riot Games · 3New York University · University of California, Irvine
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
A Game-Theoretic Decision Framework for Optimal Selection of Coordination Detection Methods in Multi-UAV Fleet Operations
Jun 1, 2026Christian Manasseh, Savana AmmonsAlgorithm Selection Air Traffic Management
Mobius Logic Inc., Tysons, VA, USA
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
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
Bilevel Optimization over Saddle Points of Zero-Sum Markov Games
May 26, 2026Zihao Zheng, Irwin King, Songtao LuGame Theory Zero-Sum Games
Department of Computer Science and Engineering, The Chinese University of Hong Kong · Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong
GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models
May 22, 2026Vartan Shadarevian, Kia Ghods, Alex Kenich +1LLM Evaluation Imperfect-Information Games
Princeton University · Google
Understanding Dynamics of Adam in Zero-Sum Games: An ODE Approach
May 19, 2026Yi Feng, Weiming Ou, Xiao WangZero-Sum Games Min-Max Optimization
Aarhus University, Aarhus, Denmark · Shanghai University of Finance and Economics, Shanghai, China · MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics, Shanghai, China
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
When and Why is Optimistic Multiplicative Weights Slow? The Geometry of Energy Dissipation
May 13, 2026John Lazarsfeld, Anas Barakat, Georgios Piliouras +2Nash Equilibrium Last-Iterate Convergence
SUTD · Google DeepMind · NUS CQT +2
Offline Two-Player Zero-Sum Markov Games with KL Regularization
May 13, 2026Claire Chen, Yuheng Zhang, Xinyu Liu +3Nash Equilibrium Reinforcement Learning
1California Institute of Technology. · University of Illinois Urbana-Champaign. · University of Virginia. +2
Equilibrium Residuals Expose Three Regimes of Matrix-Game Strategic Reasoning in Language Models
May 11, 2026Wenhua Nie, Binhan Luo, Zijie Meng +2Nash Equilibrium LLM Evaluation
National Taiwan University
Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions
May 10, 2026Soumita Hait, Ping Li, Haipeng Luo +1Last-Iterate Convergence Imperfect-Information Games
University of Southern California · Shanghai University of Finance and Economics · University of Iowa
The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
Apr 17, 2026Côme Fiegel, Pierre Ménard, Tadashi Kozuno +2Nash Equilibrium Last-Iterate Convergence
ENSAE Paris - CREST, Palaiseau, France · Inria - FairPlay · ENS Lyon, Lyon, France +3
Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier
Apr 16, 2026Come Fiegel, Pierre Menard, Tadashi Kozuno +2Last-Iterate Convergence Imperfect-Information Games
ENSAE Paris – CREST, France · ENS Lyon, France · Isara Labs +2
Local exponential stability of mean-field Langevin descent-ascent and associated particle system
Feb 2, 2026Geuntaek Seo, Minseop Shin, Pierre Monmarché +1Nash Equilibrium Lyapunov Stability
Decentralized Best-Response-Based Learning in Two-Player Zero-Sum Stochastic Games: A Finite-Sample Analysis
Sep 2, 2024Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar +2Nash Equilibrium Game Theory
IE, Purdue University. · ECE, University of Maryland, College Park. · CMS, Caltech. +1
Expected flow networks in stochastic environments and two-player zero-sum games
Oct 4, 2023Marco Jiralerspong, Bilun Sun, Danilo Vucetic +4Game-Playing Agents Zero-Sum Games
Mila – Qu´ebec AI Institute, Universit´e de Montr´eal
Stability and Convergence of Optimistic Exponential Weights with Asymmetric Step Sizes in Bimatrix Games
Date pendingHédi Hadiji, Sarah SachsNash Equilibrium Last-Iterate Convergence
Laboratoire des Signaux et Systèmes, CentraleSupélec, Paris, France · School of Mathematics, University of Bristol, Bristol, United Kingdom

Oct 5, 2026, cs.GTJ/K move · Enter open · S save

Feedback Dominance Analysis for Pursuit-Evasion Games on Graphs

Yue Guan, Daigo Shishika, Dipankar Maity, Michael Dorothy+1

School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA. · College of Engineering and Computing, George Mason University, Fairfax, VA, USA. · Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC, USA.+1

Game Theory Zero-Sum Games

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This work identifies the dominance regions for discrete, simultaneous-move pursuit-evasion games on graphs. Existing geometric approaches provide efficient characterizations of winning regions, but typically provide only sufficient conditions and rely on open-loop strategies. To address these challenges, we develop a set-based dynamic programming approach to characterize the pursuer's winning and losing regions, providing necessary and sufficient winning conditions under worst-case behavior. The reachability analysis admits a set-chasing interpretation, allowing translation of dominance sets to feedback strategies that adapt to the players' positions in real time. For states where neither player can guarantee victory, we introduce an instantaneous matrix-game formulation and establish upper and lower bounds on the pursuer's winning probability. Simulation results validate the correctness of the dominance-region characterization and the proposed bounds.

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