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Ditch expensive, rendering-based RL for autonomous driving: PerlAD uses offline data to train agents in a fast, vector-space pseudo-simulation, outperforming prior methods by 10% on driving score.
Autonomous driving models can learn to avoid accidents *before* they happen by training on expert interventions and anticipating errors.
