A Closed-Loop Evaluation of Capability Loss and Recovery in Compressed Driving Policies

The paper introduces a stage‑wise closed‑loop evaluation method to track how compression techniques affect learned driving policies. Using a POMDP formulation and PPO‑trained belief‑state policy in Gym‑Duckietown, the authors compress the actor step by step and test it across five driving curricula. They find that structured pruning first degrades driving capability, distillation can partially recover it but is limited by rehearsal data, and integer quantization further loses curricula requiring stopping and resuming, whereas the unpruned actor retains all curricula.

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• reinforcement-learning
• efficiency