# Multi-agent Reinforcement Learning for Networked System Control

arXiv: https://arxiv.org/abs/2004.01339 · HTML: https://mlsanity.com/paper/2004.01339/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 30 of 53)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Multi-agent Embodied AI: Advances and Future Directions](https://mlsanity.com/paper/2505.05108/) (2025)
- [Multi-agent Reinforcement Learning: A Comprehensive Survey](https://mlsanity.com/paper/2312.10256/) (2023)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [A Review of Cooperation in Multi-agent Learning](https://mlsanity.com/paper/2312.05162/) (2023)
- [Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic](https://mlsanity.com/paper/2111.06318/) (2021)
- [An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility](https://mlsanity.com/paper/2203.07676/) (2022)
- [Generative Emergent Communication: Large Language Model is a Collective World Model](https://mlsanity.com/paper/2501.00226/) (2024)
- [A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management](https://mlsanity.com/paper/2306.07542/) (2023)
- [The Five Ws of Multi-Agent Communication: Who Talks to Whom, When, What, and Why -- A Survey from MARL to Emergent Language and LLMs](https://mlsanity.com/paper/2602.11583/) (2026)
- [Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale](https://mlsanity.com/paper/2403.00222/) (2024)
- [SRMT: Shared Memory for Multi-agent Lifelong Pathfinding](https://mlsanity.com/paper/2501.13200/) (2025)
- [Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.06042/) (2021)
- [OnSlicing: Online End-to-End Network Slicing with Reinforcement Learning](https://mlsanity.com/paper/2111.01616/) (2021)
- [Scalable Model-based Policy Optimization for Decentralized Networked Systems](https://mlsanity.com/paper/2207.06559/) (2022)
- [Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control](https://mlsanity.com/paper/2310.02435/) (2023)
- [Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2502.19717/) (2025)
- [MDPGT: Momentum-based Decentralized Policy Gradient Tracking](https://mlsanity.com/paper/2112.02813/) (2021)
- [Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control Problem](https://mlsanity.com/paper/2104.10917/) (2021)
- [Learning to Share in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2112.08702/) (2021)
- [Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control](https://mlsanity.com/paper/2210.00945/) (2022)
- [Provable Policy Gradient Methods for Average-Reward Markov Potential Games](https://mlsanity.com/paper/2403.05738/) (2024)
- [Bayesian Ego-graph Inference for Networked Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.16606/) (2025)
- [Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems](https://mlsanity.com/paper/2110.15926/) (2021)
- [Fully Independent Communication in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2401.15059/) (2024)
- [Approximate Global Convergence of Independent Learning in Multi-Agent Systems](https://mlsanity.com/paper/2405.19811/) (2024)
- [Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems](https://mlsanity.com/paper/2212.06357/) (2022)
- [Optimistic Policy Gradient in Multi-Player Markov Games with a Single Controller: Convergence Beyond the Minty Property](https://mlsanity.com/paper/2312.12067/) (2023)
- [Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control](https://mlsanity.com/paper/2407.08964/) (2024)
- [CoordLight: Learning Decentralized Coordination for Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2603.24366/) (2026)

## Abstract (arXiv)

This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent. Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL. Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.