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What HD-Map Rewards Buy: Closed-Loop Reinforcement Learning of a Vision–Language–Action Driving Model in Neural Reconstructions
End-to-end driving policies trained by imitation learning are optimized to reproduce recorded trajectories rather than to respect the rules of the road, and their failures compound in closed loop. This work asks whether reward terms derived from a high-definition (HD) map can improve the closed-loop
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