This paper proposes decentralized multi-agent reinforcement learning (MARL) as a paradigm for enhancing the resilience of critical infrastructures. It argues that MARL's properties, such as scalability, privacy, robustness, and adaptive interaction, align well with the demands of complex, distributed systems. The research highlights credit assignment and communication as key challenges for practical MARL deployment in these environments and outlines a future research agenda focused on addressing these issues. AI
IMPACT This research could lead to more robust and adaptive control systems for essential services like power grids and transportation networks.
RANK_REASON The cluster contains two identical arXiv papers discussing a research topic.
- arXiv
- Critical Infrastructures: What Makes an Infrastructure Critical?
- Decentralized Multi-agent Reinforcement Learning
- Multi-agent reinforcement learning
- causality
- communication
- coordination
- privacy
- reinforcement learning
- robustness
- scalability
AI-generated summary · Google Gemini · from 2 sources. How we write summaries →
