Three variable control is commonly used as the underlying control algorithm in earthquake simulation shaking table,in-volved numerous parameters in the process of parameter tuning,and traditional parameter tuning methods suffer from problems such as low efficiency and complicated processes.In order to improve tuning efficiency and accuracy,a novel parameter tuning method for three variable control of shaking table based on the deep deterministic policy gradient(DDPG)algorithm was proposed.Taking the three vari-able control system as a reinforcement learning environment,the DDPG algorithm was used to learn and train the state-action-reward of the system,the optimal control parameters were obtained.The tuning parameters were then tested in shaking table and compared with traditional tuning methods.The results show that the DDPG algorithm can effectively optimize the control performance of the shaking ta-ble and improve the accuracy and reliability of experimental results,which has practical application value.
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