Structured Pruning Based on Reinforcement Learning for CNN

: In recent years, deep learning models have demonstrated excellent performance in complex tasks, but their large number of parameters and high computational costs have limited their application in resource-constrained scenarios. This paper proposes a structured pruning method based on reinforcement learning (TD3 algorithm), which performs structured pruning on a group-by-group basis to balance model compression efficiency and performance retention. The TD3 agent takes the parameter states of each group as observation inputs, dynamically adjusts the pruning rate of each group as actions, and designs a multi-objective reward function based on model accuracy,FLOPs, and the number of parameters to achieve autonomous optimization of pruning strategies. Experiments on ResNet56 and VGG19 with the CIFAR-100 dataset show that this method maintains high classification accuracy while significantly reducing parameters and computational complexity. Compared with traditional pruning methods, it is more adaptive and provides an effective solution for model deployment in resource-constrained environments.