###### Abstract
[22] presented a combined-vibration-control method for a hybrid-structured flexible manipulator based on sliding-mode control and reinforcement learning. The experimental results showed that the combined control method had good robustness to the tip trajectory and tip-load mass under the condition that the learning parameters of the controller remained unchanged. In terms of the vibration-control problems of magneto rheological dampers [23; 24], as the controller was designed based on Q-learning [28], they are theoretically not suitable for high-dimensional continuous-action spaces. Park et al. [23] proposed a novel reinforcement-learning method based on Q-learning for the vibration control of a magneto rheological elastomer. The experimental results showed that the proposed method could minimize the vibration level with respect to the tonal disturbance or sine-sweep disturbance. Yuan et al. [24] proposed a semiactive control strategy of a magneto rheological damper based on the Q-learning algorithm, and the simulation results showed that it was better than simple bang-bang control. In the vibration-control problems of flexible beams or plates [25; 26; 27], accurate finite element models are constructed as the simulation environment, and then reinforcement-learning algorithms, such as the soft actor-critic algorithm, DDPG algorithm and multiagent twin delayed DDPG algorithm, are applied to train the vibration controllers. Finally, the well-trained controllers are validated in experiments. The simulation and experimental results demonstrate that the controllers trained by the proposed RL algorithms have better control effects compared with PD control. However, an accurate finite element model is difficult to build for complex vibrating systems, and the proper PD parameters are difficult to estimate. Although RL algorithms were applied in the above vibration-control research, they mainly considered vibration-control problems of low dimension (for instance, the single-input suspension control problem) and simple dynamics, such as link, beam, plate, quarter car, etc., simulation models. However, this paper solved the multi-input/multi-output real-world vibration-control problem within a frequency range by using RL and an FIR filter, which was inspired by the least-mean-square (LMS) adaptive algorithm. To the best of the authors' knowledge, this is the first time that the high-dimensional high-frequency vibration-control problem has been solved by using reinforcement learning. In this paper, the FIR filter was used to establish the transfer-function channels (i.e., the simulator) from the exciter and actuators to the sensors. Then, a reinforcement-learning algorithm interacted with the simulator to find a near-optimal control policy to meet the specified goals. Furthermore, the RL-based controller was verified in high-dimensional high-frequency vibration-control experiments. The rest of this paper is organized as follow. First, the vibration-control problem is formulated. Second, a new method for a vibration-controller design through RL is presented. Third, the RL-designed vibration controller is experimentally verified. Finally, the conclusions are drawn.