Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives
arXiv:2601.01665v2 Announce Type: replace Abstract: Deep reinforcement learning (DRL) has shown great promise in addressing multi-objective combinatorial optimization problems (MOCOPs). Nevertheless, the robustness of these learning-based solvers has remained insufficiently explored, especially across diverse and complex problem distributions. In this paper, we propose a unified robustness-oriented framework for preference-conditioned DRL solvers for MOCOPs. Within this framework, we develop a preference-based adversarial attack to generate hard instances that expose solver weaknesses, and qua
The increasing deployment of DRL in real-world multi-objective optimization necessitates improved robustness against diverse and adversarial conditions.
Ensuring the robustness of DRL-based solvers is critical for their reliable application in complex, real-world systems, preventing catastrophic failures from unexpected inputs.
This research introduces methods to proactively identify and mitigate vulnerabilities in AI systems designed for combinatorial optimization, enhancing their trustworthiness and deployment readiness.
- · AI developers
- · Logistics and supply chain
- · Autonomous systems
- · Critical infrastructure management
- · Organizations relying on brittle DRL systems
- · Hackers exploiting AI vulnerabilities
More resilient AI deployments in complex decision-making scenarios.
Increased adoption of AI in high-stakes optimization problems previously deemed too risky.
Development of new regulatory frameworks for AI robustness and adversarial testing.
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