# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 1 Introduction
The newly designed adaptive manipulator controller realizes a speed-free filter. Their simulation results verify the effectiveness of the human-computer interaction system based on the real manipulator parameters. On the other hand, [PERSON] et al. [19] proposed a learning framework for robot manipulators based on nonlinear model predictive control, which aims to solve the motion planning problem of robot manipulators in the presence of obstacles. The cost function, system constraints, and manipulator model were parameterized by using the value function of the reinforcement learning framework and the myopic value of the action-value function. The controller was applied to the robot manipulator of a 6-DOF model. Their numerical simulation results show that this controller can effectively control the attitude of the end effector, avoid the collision between the manipulator and the obstacle, and improve the efficiency of the control loop by 21%. Additionally, [PERSON] et al. [20] designed a two-wheeled inverted pendulum mobile manipulator for retail shelf inspections. In comparison to PID control, in simulation tests, the controller based on reinforcement learning exhibited stronger robustness against variations in initial conditions, changes in inertia parameters, and disturbances applied to the robot. In the field of magnetorheological damper vibration control [21; 22], [PERSON] et al. [21] proposed a reinforcement learning method for magnetorheological elastomer vibration control based on Q-learning. Their research shows that it is feasible to use a model-free reinforcement learning model to realize an adaptive controller based on the application of highly nonlinear magnetorheological elastomers. When compared with a model without the controller, this method suppressed the vibration level by up to 57%. In addition, [PERSON] et al. [22] proposed a magnetorheological damping semi-active control strategy based on a reinforcement learning Q-learning algorithm. Their simulation results show that this method is simpler, easier to implement, and more robust. They used the reinforcement learning semi-active control strategy to study the vibration reduction of a two-story frame structure. The results show that the strategy is superior to the simple bang-bang control method. At present, some progress has been made in the combination of reinforcement learning and finite-impulse response filters. [PERSON] et al. [26] successfully solved the multi-input/multi-output actual vibration control problem in a frequency range by using RL and FIR filters, which marks the first time that reinforcement learning has been used to solve high-dimensional high-frequency vibration control problems. In order to meet the lightweight requirements of the underlying equipment, they designed the simplest fully connected neural network. However, this method may face the challenge of overfitting or underfitting when solving more complex and variable dynamic problems. In order to solve this problem, we try to find a way to both maintain lightweightness and solve complex problems. In the field of deep learning, the way to achieve lightweightness is to reduce resource consumption and model complexity. In this regard, some achievements have been made [27; 28; 29; 30]. Among the research on lightweightness, [PERSON] et al. [27] proposed a residual network framework to reduce the burden of network training. They explicitly redefined each layer as the input of the reference layer and learned the residual function instead of learning an unknown function. Through comprehensive experimental data, they clarified that their residual network is easier to optimize, and that increasing the network depth can achieve higher accuracy. This method improved the results in the COCO target detection dataset by 28%, and won the first place in the COCO target detection and image segmentation tasks. On the other hand, [PERSON] et al. [28] proposed an efficient model called MobileNets, which uses deep separable convolution to construct a lightweight deep neural network.