mlsanityData to 7 Oct 2026

The Confluence of Networks, Games and Learning

arXiv 2105.08158 ↗First posted 17 May 2021cs.MA cs.AI cs.LGcited by 26

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Abstract (arXiv)

Recent years have witnessed significant advances in technologies and services in modern network applications, including smart grid management, wireless communication, cybersecurity as well as multi-agent autonomous systems. Considering the heterogeneous nature of networked entities, emerging network applications call for game-theoretic models and learning-based approaches in order to create distributed network intelligence that responds to uncertainties and disruptions in a dynamic or an adversarial environment. This paper articulates the confluence of networks, games and learning, which establishes a theoretical underpinning for understanding multi-agent decision-making over networks. We provide an selective overview of game-theoretic learning algorithms within the framework of stochastic approximation theory, and associated applications in some representative contexts of modern network systems, such as the next generation wireless communication networks, the smart grid and distributed machine learning. In addition to existing research works on game-theoretic learning over networks, we highlight several new angles and research endeavors on learning in games that are related to recent developments in artificial intelligence. Some of the new angles extrapolate from our own research interests. The overall objective of the paper is to provide the reader a clear picture of the strengths and challenges of adopting game-theoretic learning methods within the context of network systems, and further to identify fruitful future research directions on both theoretical and applied studies.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 37 of its 131 references that are in the map. First posted on arXiv 17 May 2021.

Builds on

37 of 131 references.

References · 37

  1. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
  2. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  3. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  4. 2015A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insightscited by 203
  5. 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 189
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  11. 2020Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexitycited by 91
  12. 2016Learning in games with continuous action sets and unknown payoff functionscited by 78
25 more
  1. 2018On Gradient-Based Learning in Continuous Gamescited by 46
  2. 2018Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimizationcited by 40
  3. 2018Bandit learning in concave N-person gamescited by 32
  4. 2020Fictitious play in zero-sum stochastic gamescited by 24
  5. 2016Riemannian game dynamicscited by 18
  6. 2019A Dynamic Games Approach to Proactive Defense Strategies against Advanced Persistent Threats in Cyber-Physical Systemscited by 16
  7. 2021Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexitycited by 15
  8. 2019Global Convergence of Policy Gradient for Sequential Zero-Sum Linear Quadratic Dynamic Gamescited by 14
  9. 2019On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learningcited by 12
  10. 2020Blackwell Online Learning for Markov Decision Processescited by 12
  11. 2018On Passivity, Reinforcement Learning and Higher-Order Learning in Multi-Agent Finite Gamescited by 8
  12. 2017A Passivity-Based Approach to Nash Equilibrium Seeking over Networkscited by 5
  13. 2019Combining No-regret and Q-learningcited by 4
  14. 2013Stationary Markov Perfect Equilibria in Discounted Stochastic Gamescited by 2
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  20. 2016A Game-Theoretic Framework for Resilient and Distributed Generation Control of Renewable Energies in Microgridscited by 1
  21. 2016On convergence rates of game theoretic reinforcement learning algorithmscited by 1
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 6; 2024: 8; 2025: 6; 2026: 2.

26 papers.

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  1. 2022Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applicationscited by 27
  2. 2024Conjectural Online Learning with First-order Beliefs in Asymmetric Information Stochastic Gamescited by 14
  3. 2022Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptationcited by 12
  4. 2024Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Searchcited by 12
  5. 2023Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement Learningcited by 11
  6. 2025Game Theory Meets LLM and Agentic AI: Reimagining Cybersecurity for the Age of Intelligent Threatscited by 10
  7. 2023Scalable Learning of Intrusion Responses through Recursive Decompositioncited by 8
  8. 2024Intrusion Tolerance for Networked Systems through Two-Level Feedback Controlcited by 8
  9. 2024Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attackscited by 8
  10. 2023A First Order Meta Stackelberg Method for Robust Federated Learningcited by 7
  11. 2024Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfarecited by 7
  12. 2022Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity Analysiscited by 5
14 more
  1. 2024Multi-level Traffic-Responsive Tilt Camera Surveillance through Predictive Correlated Online Learningcited by 5
  2. 2023Is Stochastic Mirror Descent Vulnerable to Adversarial Delay Attacks? A Traffic Assignment Resilience Studycited by 4
  3. 2023Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learningcited by 4
  4. 2025Reinforcement Learning with Physics-Informed Symbolic Program Priors for Zero-Shot Wireless Indoor Navigationcited by 4
  5. 2025Agentic AI for Cyber Resilience: A New Security Paradigm and Its System-Theoretic Foundationscited by 4
  6. 2024Learning from Response not Preference: A Stackelberg Approach for LLM Detoxification using Non-parallel Datacited by 3
  7. 2025From Texts to Shields: Convergence of Large Language Models and Cybersecuritycited by 3
  8. 2025In-Context Reinforcement Learning via Communicative World Modelscited by 3
  9. 2024Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for Urban Transportation Managementcited by 2
  10. 2022Commitment with Signaling under Double-sided Information Asymmetrycited by 1
  11. 2023Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe Self-Driving in Non-Stationary Environmentscited by 1
  12. 2026Distributed Online Convex Optimization with Nonseparable Costs and Constraintscited by 1
  13. 2026Agentic Incident Response through Digital Twin-Enhanced Multiscale Planningcited by 1
  14. 2025AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learningcited by 0