OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
This work proposes OPTED (on-policy fine-tuning for end-to-end driving) which decouples reinforcement learning from the post-training of the end-to-end policy: a privileged teacher is trained using RL on vectorized inputs (HD-map and bounding boxes) which provides supervision to the pre-trained student during closed-lo...
D. Da Col, M. Igl, Peter Karkus et al.
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