Adversarial Training for Robust Coverage Network under Worst-case Facility Losses

Researchers propose a Dual-Agent Deep Reinforcement Learning framework to solve the Maximal Covering Location-Interdiction Problem, a computationally complex bi-level optimization challenge critical for resilient infrastructure planning. The adversarial training approach, where location and interdiction agents compete, achieves superior computational efficiency while maintaining competitive solution quality across synthetic and real-world datasets.

This research addresses a fundamental challenge in infrastructure resilience by tackling the MCLIP problem through an innovative adversarial machine learning lens. Traditional optimization methods struggle with the strong coupling between the facility location decision layer and the worst-case interdiction layer, making this a genuinely difficult computational problem with real-world applications in telecommunications, power grids, and emergency services.

The proposed DADRL framework represents a paradigm shift in how complex bi-level optimization problems are approached. Rather than solving sequentially, the dual-agent architecture trains location and interdiction agents simultaneously, creating dynamic feedback that naturally captures competitive interplay. The surrogate-based ensemble inference strategy is particularly clever, leveraging the trained interdiction agent's learned heuristics to guide location decisions more intelligently than brute-force methods.

For infrastructure operators and planners, this methodology could significantly reduce computational time for network design while improving robustness against adversarial scenarios. The model-agnostic nature means the framework applies across various network topologies and infrastructure domains. The adversarial learning paradigm also establishes a blueprint for other bi-level optimization problems in resource allocation, game-theoretic scenarios, and security planning.

Looking forward, real-world implementation requires integration with existing infrastructure planning software and validation against domain-specific constraints. The research's extensibility to other bi-level problems positions it as a foundational contribution to computational optimization, potentially attracting interest from infrastructure technology vendors and government agencies prioritizing resilience planning.

• →Dual-agent adversarial reinforcement learning effectively solves the computationally intractable Maximal Covering Location-Interdiction Problem
• →Simultaneous training against evolving adversary naturally captures dynamic competitive interplay between optimization levels
• →Surrogate-based ensemble inference strategy improves location agent decisions using trained interdiction agent capabilities
• →Framework achieves superior computational efficiency while maintaining competitive solution quality versus traditional baselines
• →Model-agnostic approach extends beyond infrastructure to other bi-level optimization problems across domains