On-board precision equipment installed in vehicles is susceptible to vibrations caused by irregularities on the road surface, which may decrease accuracy and damage to components. Installing a vibration absorption system in the vehicle is necessary to mitigate the impact of vehicle vibrations on the equipment. Vibration systems widely use vibration absorbers based on magnetorheological elastomer (MRE). Previous research has mainly used Proportional-Integral-Derivative (PID), fuzzy control, and ON-OFF algorithms to control the vibration absorption system. However, these algorithms exhibit poor control accuracy and adaptability in uncertain environments. To enhance the adaptive capabilities of vibration absorbers in time-varying environments and to maximize their performance, this study explores the use of the Twin Delayed Deep Deterministic Policy Gradient (TD3) control algorithm. Additionally, we proposed an Immune Optimization Twin Delayed Deep Deterministic Policy Gradient (IO-TD3) control algorithm to address the low-efficiency issue in the initial training stages. Simulation results show that, compared to the TD3 algorithm, Double Q-Deep Deterministic Policy Gradient (Double Q-DDPG) algorithm, Deep Deterministic Policy Gradient (DDPG) algorithm, and Deep Q-Network (DQN) algorithm, the IO-TD3 algorithm significantly exhibits faster convergence speed and improved stability. Finally, we apply the IO-TD3 algorithm to the MRE absorber controller and verify the controller's performance using the Monte Carlo method. The results show that the controller using the IO-TD3 control algorithm has high control accuracy and stability.