The invention relates to the technical field of industrial control, and particularly discloses an industrial blending tank control method based on a deep
reinforcement learning model, and the method comprises the steps: building a blending tank
simulation system as an interaction environment of deep
reinforcement learning; a deep
reinforcement learning network of an Actor-critic structure is set; an experience replay
pool is constructed based on a deep reinforcement
learning network and an interaction environment, and an Actor network and a critic network are respectively trained based on the experience replay
pool to obtain a strategy model and an evaluation model. According to the method, a large amount of
empirical data is obtained through interaction with a simulated production device or
system, learning is carried out from the large amount of
empirical data, so that a control strategy is continuously optimized, the mode is totally different from PID and is similar to the thought that prediction is carried out before control of MPC, but
system identification and parameter setting do not need to be carried out, and therefore the control strategy is optimized. A large amount of complex work is avoided, learning is completely carried out from data, and a certain generalization ability is achieved.