用于弹性关键基础设施的去中心化多智能体强化学习
Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures
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中文总结 AI 辅助
研究用于弹性关键基础设施的去中心化多智能体强化学习，基于对其特性与基础设施要求的分析，指出信用分配和通信是实际可行的核心条件，提出包含结构、因果、弹性感知信用分配及相关通信等的研究议程，为弹性关键基础设施构建有条件的基础。
中文摘要 AI 辅助
关键基础设施日益分布式、相互依赖且易受不断演变的干扰，弹性成为其运行和控制的核心要求。本文认为去中心化多智能体强化学习不应仅被视为集中训练分散执行的分布式替代方案，而应是与弹性关键基础设施要求在结构上一致的范式。该观点基于对去中心化多智能体强化学习特性和关键基础设施要求的分析。然而，仅结构一致不足以实际部署，本文确定信用分配和通信是实际可行性的两个核心条件。基于这些挑战，本文提出了一个研究议程，包括结构感知、因果感知和弹性感知的信用分配；用于协调和信用分配的通信；以及在部署约束下的安全、及时和可恢复的去中心化学习。总体而言，本文将去中心化多智能体强化学习重新构建为弹性关键基础设施有前景但有条件的基础。
英文摘要
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.
发表机构
- School of Computer Science, University of Leeds（利兹大学计算机科学学院）
- School of Energy Systems, LUT University（卢特大学能源系统学院）
机构由 AI 辅助整理，请以论文原文为准。
