Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures

2026-07-20

AI Summary

This work proposes a resilience-oriented decentralized multi-agent reinforcement learning (MARL) framework tailored for distributed, interdependent, and disturbance-prone critical infrastructure systems. By integrating structure-aware, causality-aware, and resilience-aware credit assignment mechanisms alongside a communication strategy that jointly optimizes coordination and credit allocation, the approach aligns local autonomy with system-level objectives. The resulting learning framework exhibits strong scalability, privacy preservation, fault robustness, and adaptive coordination capabilities. Furthermore, the study delineates the theoretical and practical conditions necessary for deploying resilient MARL systems under real-world constraints, thereby establishing a novel paradigm for resilience-driven control of critical infrastructure.

Abstract

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.

Problem

• decentralized MARL
• resilient critical infrastructures
• credit assignment
• communication
• coordination

Innovation

• decentralized MARL
• resilient critical infrastructures
• credit assignment
• communication
• structure-aware learning