Safety Aware Neural Pruning for Deep Reinforcement Learning (Student Abstract)
Keywords:Reinforcement Learning, Neural Pruning, Safe Reinforcement Learning
AbstractNeural 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.
How to Cite
Gangopadhyay, B., Dasgupta, P., & Dey, S. (2023). Safety Aware Neural Pruning for Deep Reinforcement Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16212-16213. https://doi.org/10.1609/aaai.v37i13.26966
AAAI Student Abstract and Poster Program