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Adversarial training with deep reinforcement learning cracks the computationally intractable Maximal Covering Location-Interdiction Problem, outperforming traditional methods in speed and solution quality.
DRL agents can now solve vehicle routing problems across diverse real-world distributions thanks to a new architecture that adaptively recombines specialized policy modules.
