Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement
Multi-task vehicle routing problem (VRP) solvers seek to handle multiple VRP variants within a single unified model, avoiding the need to train a separate model for every variant. In spite of recent progress, current approaches remain limited on two fronts. On the training side, reinforcement learning suffers from reward-scale disparities and shrinking advantage signals as policies improve, whereas preference optimization stagnates once sampled tours become near-identical and thus fundamentally limited by the quality of the policy's own generated solutions, leaving both paradigms with weak sup
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- PossiblePossibly related (embedding) · 50%Adaptive primal–dual Q-learning for electric vehicle route optimization on real-world charging networks - Nature →
- PossiblePossibly related (embedding) · 49%Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment →
- LinkedLinked via arxiv author · 85%Arthur Corrêa →
“Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement”
- LinkedLinked via arxiv author · 85%Paulo Nascimento →
“Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement”
- LinkedLinked via arxiv author · 85%Samuel Moniz →
“Improving Cross-Problem Vehicle Routing with Locally Augmented Preferences and Representation Disentanglement”