# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 1 Introduction
Through extensive resource and accuracy experiments, MobileNets showed strong performance compared with other popular ImageNet classification models. They also demonstrate the effectiveness of this method in object detection, fine-grained classification, face attributes, and large-scale geolocation. [PERSON] et al. [29] proposed a computationally efficient CNN architecture called ShuffleNet. This method uses two new operations, point group convolution, and channel rearrangement, which greatly reduces the computational cost while maintaining accuracy. Their experiments on ImageNet classification and MS COCO target detection show that ShuffleNet is superior to other structures. On ARM-based mobile devices, the speedup of ShuffleNet is about 13 times higher than that of AlexNet, and it maintains considerable accuracy. In addition, [PERSON] et al. [30] proposed a deep residual shrinkage network to improve the feature learning ability of high-noise vibration signals. They inserted the adaptive soft threshold as a nonlinear transformation layer into the deep architecture to eliminate unimportant features. The effectiveness of their developed method was verified through various types of noise experiments. At present, reinforcement learning forms a very mature system. It is mainly divided into two categories: value-based learning and probability-based learning, and has algorithms that combine these two methods. Probability-based reinforcement learning is one of the most direct methods and makes decisions by directly outputting the probability of the next possible action. In this method, each action has the opportunity to be selected, but the size of these opportunities is different. For example, the Policy Gradients approach [31] is a probability-based approach. In contrast, the value-based approach involves outputting the value of all actions and selecting the action with the highest value in the decision-making process. For example, algorithms such as Q-learning [32] are value-based methods. Combining the advantages of these two methods, some more optimized algorithms have been derived, such as Actor-Critic [33]. In this structure, the Actor makes an action based on probability, while the Critic assesses the value of the action. This combination optimizes the original Policy Gradients approach and accelerates the learning process. At present, the AC method has many extensions, including the Deep Deterministic Policy Gradient (DDPG) [34], Soft Actor-Critic (SAC) [35], and Twin Delayed Deep Deterministic Policy Gradient (TD3) [36] techniques. Regarding the Actor-Critic method of reinforcement learning, [PERSON] et al. [34] proposed an Actor-Critic model-free algorithm based on Deterministic Policy Gradients, which can operate in a continuous-action space. They successfully solved the challengesof more than 20 simulated physical tasks by using the same learning algorithm, network architecture, and hyperparameters. [PERSON] et al. (2018) described the Soft Actor-Critic approach, which is an offline strategy Actor-Critic algorithm based on a reinforcement learning framework. Its goal is to maximize both the expected return and entropy. This method is superior to the previous online strategy and offline strategy methods in terms of the sample utilization efficiency and progressive performance. In addition, [PERSON] et al. (2018) found that in value-based reinforcement learning methods, known function approximation errors may lead to overestimation and suboptimal strategies for value estimation. Based on this, they propose a new mechanism called Twin Delayed Deep Deterministic Policy Gradient to mitigate the overestimation issues of Actor-Critic algorithms. They suggest constraining overestimation by taking the minimum of a pair of critics based on the foundation of double Q-learning. When evaluating their approach, it consistently outperformed current state-of-the-art methods across various testing environments.