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Proportional-integral-derivative (PID) controllers have been widely used inthe process industry. However, the satisfactory control performance of a PIDcontroller depends strongly on the tuning parameters. Conventional PID tuningmethods require extensive knowledge of the system model, which is not alwaysknown especially in the case of complex dynamical systems. In contrast,reinforcement learning-based PID tuning has gained popularity since it cantreat PID tuning as a black-box problem and deliver the optimal PID parameterswithout requiring explicit process models. In this paper, we present a novelentropy-maximizing twin-delayed deep deterministic policy gradient (EMTD3)method for automating the PID tuning. In the proposed method, anentropy-maximizing stochastic actor is employed at the beginning to encouragethe exploration of the action space. Then a deterministic actor is deployed tofocus on local exploitation and discover the optimal solution. Theincorporation of the entropy-maximizing term can significantly improve thesample efficiency and assist in fast convergence to the global solution. Ourproposed method is applied to the PID tuning of a second-order system to verifyits effectiveness in improving the sample efficiency and discovering theoptimal PID parameters compared to traditional TD3.

A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning