Safety Aware Neural Pruning for Deep Reinforcement Learning (Student Abstract)

Briti Gangopadhyay, Pallab Dasgupta, Soumyajit Dey

AAAI 2023 pp. 16212-16213

doi:10.1609/AAAI.V37I13.26966 /aaai/2023/gangopadhyay2023aaai-safety/

Abstract

Neural network pruning is a technique of network compression by removing weights of lower importance from an optimized neural network. Often, pruned networks are compared in terms of accuracy, which is realized in terms of rewards for Deep Reinforcement Learning (DRL) networks. However, networks that estimate control actions for safety-critical tasks, must also adhere to safety requirements along with obtaining rewards. We propose a methodology to iteratively refine the weights of a pruned neural network such that we get a sparse high-performance network without significant side effects on safety.

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Cite

Text

Gangopadhyay et al. "Safety Aware Neural Pruning for Deep Reinforcement Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26966

Markdown

[Gangopadhyay et al. "Safety Aware Neural Pruning for Deep Reinforcement Learning (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/gangopadhyay2023aaai-safety/) doi:10.1609/AAAI.V37I13.26966

BibTeX

@inproceedings{gangopadhyay2023aaai-safety,
  title     = {{Safety Aware Neural Pruning for Deep Reinforcement Learning (Student Abstract)}},
  author    = {Gangopadhyay, Briti and Dasgupta, Pallab and Dey, Soumyajit},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16212-16213},
  doi       = {10.1609/AAAI.V37I13.26966},
  url       = {https://mlanthology.org/aaai/2023/gangopadhyay2023aaai-safety/}
}