###### Abstract  
Deep neural networks (DNNs) have achieved great success in the field of computer vision. The high requirements for memory and storage by DNNs make it difficult to apply them to mobile or embedded devices. Therefore, compression and structure optimization of deep neural networks have become a hot research topic. To eliminate redundant structures in deep convolutional neural networks (DCNNs), we propose an efficient filter pruning framework via deep reinforcement learning (DRL). The proposed framework is based on a deep deterministic policy gradient (DDPG) algorithm for filter pruning rate optimization. The main features of the proposed framework are as follows: (1) AA tailored reward function considering both accuracy and complexity of DCNN is proposed for the training of DDPG and (2) a novel filter sorting criterion based on Taylor expansion is developed for filter pruning selection. To illustrate the effectiveness of the proposed framework, extensive comparative studies on large public datasets and well-recognized DCNNs are conducted. The experimental results demonstrate that the Taylor-expansion-based filter sorting criterion is much better than the widely used minimum-weight-based criterion. More importantly, the proposed filter pruning framework can achieve over 10\(\times\) parameter compression and 3\(\times\) floating point operations (FLOPs) reduction while maintaining similar accuracy to the original network. The performance of the proposed framework is promising compared with state-of-the-art DRL-based filter pruning methods.  
deep convolutional neural network; filter pruning; deep deterministic policy gradient; Taylor expansion +
Footnote †: journal:  
The filter-level DCNN pruning method discussed in this paper mainly focuses on the following two problems: (1) The determination of the filter pruning rate in each layer and (2) the ordering of filters to be pruned, which is known as the filter-sorting problem. To solve these two problems, this paper proposes a filter-level DCNN pruning method based on RL, termed FPRL. In the proposed FPRL, the filter pruning rate determination problem is formulated as a Markov decision process (MDP), which is solved by a deep deterministic policy gradient (DDPG) algorithm. Furthermore, the Taylor expansion criterion is considered for filter ordering  
The major contributions of this paper can be summarized as follows:  
* A DDPG-based automatic DCNN pruning method is proposed, which prunes the redundant filters in the network to compress the network.
* The DDPG algorithm optimizes the pruning rate of each layer. A tailored reward function considering the change of both network accuracy and complexity before and after layer-wise pruning is developed for DDPG training.
* A Taylor-expansion-based filter ranking criterion is considered for filter pruning in the FPRL method, which proves to be much more efficient than the widely used minimum-weight-based ranking criterion.
* To illustrate the efficiency of the proposed method in DCNN pruning, extensive experiments have been conducted with several classical DCNNs including VGGNet and ResNet on CIFAR10 and CIFAR100 datasets. The results demonstrate that the FPRL can achieve more than 10\(\times\) the parameter compression and 3\(\times\) the FLOPs reduction while maintaining accuracy similar to the initial network.