# Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures

arXiv: https://arxiv.org/abs/2607.18359 · HTML: https://mlsanity.com/paper/2607.18359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 33 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks](https://mlsanity.com/paper/2110.14300/) (2021)
- [Communication-Efficient Actor-Critic Methods for Homogeneous Markov Games](https://mlsanity.com/paper/2202.09422/) (2022)
- [Holarchic Structures for Decentralized Deep Learning - A Performance Analysis](https://mlsanity.com/paper/1805.02686/) (2018)
- [Discrete-choice Multi-agent Optimization: Decentralized Hard Constraint Satisfaction for Smart Cities](https://mlsanity.com/paper/2305.16993/) (2023)
- [Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning](https://mlsanity.com/paper/2311.03756/) (2023)
- [Optimization under Attack: Resilience, Vulnerability, and the Path to Collapse](https://mlsanity.com/paper/2502.05954/) (2025)
- [A Communication-Efficient Decentralized Actor-Critic Algorithm](https://mlsanity.com/paper/2510.19199/) (2025)

## Abstract (arXiv)

Critical infrastructures are increasingly distributed, interdependent, and exposed to evolving disruptions, making resilience a central requirement for their operation and control. This paper argues that decentralized multi-agent reinforcement learning (MARL) should be understood not merely as a distributed alternative to centralized training with decentralized execution but as a paradigm structurally aligned with the requirements of resilient critical infrastructures. This perspective is grounded in an analysis of the properties of decentralized MARL and the requirements of critical infrastructures, including scalability to large numbers of agents, support for privacy and local autonomy, robustness to failures, and interaction-driven adaptation among interdependent components. However, structural alignment alone is insufficient for practical deployment. This paper identifies credit assignment and communication as two central conditions for its practical feasibility. Credit assignment determines whether local learning remains aligned with system-level objectives, while communication determines whether coordination can be learned and maintained under realistic operational constraints. Building on these challenges, this paper proposes a research agenda focused on structure-aware, causality-aware, and resilience-aware credit assignment; communication for both coordination and credit assignment; and safe, timely, and recoverable decentralized learning under deployment constraints. Overall, this paper reframes decentralized MARL as a promising but conditional foundation for resilient critical infrastructures.