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

arXiv:2609.00718 · cs.AI, cs.CV · Submitted 2026-09-01 · Read on arXiv

cs.AI, cs.CV

Submitted: 2026-09-01

Updated: 2026-09-01

Code: https://github.com/duckietown/gym-duckietown

Terminology

• Actor — 41× in this paper · explained in 3 episodes
• Pruning — 37× in this paper · explained in 4 episodes
• Distillation — 35× in this paper · explained in 2 episodes
• Quantization — 34× in this paper · explained in 12 episodes
• Fidelity — 9× in this paper · explained in 2 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Knowledge Distillation — 8× in this paper · explained in 11 episodes
• Quantization-Aware Training — 4× in this paper · explained in 3 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Partially Observable Markov Decision Process — 3× in this paper
• Proximal Policy Optimization — 3× in this paper
• Adam — 2× in this paper · explained in 2 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Computer Vision and Pattern Recognition — 2× in this paper
• Partially observable Markov decision process (POMDP) — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• IEEE Intelligent Transportation Systems Conference — 1× in this paper
• International Conference on Learning Representations — 1× in this paper
• King Fahd University of Petroleum and Minerals — 1× in this paper
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• A White Paper on Neural Network Quantization
• What Do Compressed Deep Neural Networks Forget?
• Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN
• Prune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
• Proximal Policy Optimization Algorithms
• FP8 versus INT8 for efficient deep learning inference
• QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning
• Compressing Multi-Task Model for Autonomous Driving via Pruning and Knowledge Distillation

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