DRARL improves an autonomous driving policy by detecting the out-of-distribution surrounding-object behavior behind a disengagement and training in a reason-augmented simulation, outperforming log-replay and random-reason baselines in CARLA.
Autonomous driving policy continual learning with one-shot disen- gagement case,
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DRARL: Disengagement-Reason-Augmented Reinforcement Learning for Efficient Improvement of Autonomous Driving Policy
DRARL improves an autonomous driving policy by detecting the out-of-distribution surrounding-object behavior behind a disengagement and training in a reason-augmented simulation, outperforming log-replay and random-reason baselines in CARLA.