mlsanityData to 14 Oct 2026

Scalable Reinforcement Learning for Multi-Agent Networked Systems

arXiv 1912.02906 ↗First posted 5 Dec 2019math.OC cs.AI cs.LGcited by 24

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Abstract (arXiv)

We study reinforcement learning (RL) in a setting with a network of agents whose states and actions interact in a local manner where the objective is to find localized policies such that the (discounted) global reward is maximized. A fundamental challenge in this setting is that the state-action space size scales exponentially in the number of agents, rendering the problem intractable for large networks. In this paper, we propose a Scalable Actor Critic (SAC) framework that exploits the network structure and finds a localized policy that is an O(ρkappa)-approximation of a stationary point of the objective for some ρ∈(0,1), with complexity that scales with the local state-action space size of the largest kappa-hop neighborhood of the network. We illustrate our model and approach using examples from wireless communication, epidemics and traffic.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Dec 2019.

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Citing papers per year: 2022: 1; 2023: 7; 2024: 6; 2025: 5; 2026: 5.

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  1. 2023Multi-agent Reinforcement Learning: A Comprehensive Surveycited by 33
  2. 2023Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilitiescited by 15
  3. 2023Multi-Player Zero-Sum Markov Games with Networked Separable Interactionscited by 10
  4. 2025BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatchcited by 7
  5. 2024Locally Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Dependenciescited by 6
  6. 2023Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient Algorithmscited by 4
  7. 2024Representation Learning For Efficient Deep Multi-Agent Reinforcement Learningcited by 3
  8. 2024Scalable spectral representations for multi-agent reinforcement learning in network MDPscited by 3
  9. 2026Building Better Environments for Autonomous Cyber Defencecited by 3
  10. 2022Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systemscited by 2
  11. 2023Scalable Multi-Agent Reinforcement Learning with General Utilitiescited by 2
  12. 2023A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Controlcited by 2
12 more
  1. 2024Scalable Reinforcement Learning for Linear-Quadratic Control of Networkscited by 2
  2. 2025Reinforcement-Learning Portfolio Allocation with Dynamic Embedding of Market Informationcited by 2
  3. 2026Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsamplingcited by 2
  4. 2024Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approachcited by 1
  5. 2024A finite time analysis of distributed Q-learningcited by 1
  6. 2025Multi-Agent Conditional Diffusion Model with Mean Field Communication as Wireless Resource Allocation Plannercited by 1
  7. 2026Revisiting Policy Gradients for Restricted Policy Classes: Escaping Myopic Local Optima with k-step Policy Gradientscited by 1
  8. 2023A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Gamescited by 0
  9. 2025Signal attenuation enables scalable decentralized multi-agent reinforcement learning over networkscited by 0
  10. 2025Structured Cooperative Multi-Agent Reinforcement Learning: a Bayesian Network Perspectivecited by 0
  11. 2026Hybrid Offline-Online Multi-Agent Decision Transformers for Wireless Resource Managementcited by 0
  12. 2026Continual Graph Multi-Agent Reinforcement Learningcited by 0