Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures
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,...
| Authors | Minghui Ding, Evangelos Pournaras |
|---|---|
| Primary category | cs.MA |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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