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Abstract To solve the problem of temperature control in proton exchange membrane electrolytic cell (PEMEC), this paper presents a temperature control method based on multi‐experience pool probability playback and Ornstein‐Uhlenbeck noise‐twin delay depth deterministic strategy gradient. Firstly, considering the influence of water supply, anode and cathode pressure, and natural heat dissipation on temperature, a refined thermal model of PEMEC is established and transformed into a Markov model under the framework of deep reinforcement learning (DRL). Then, to solve the training instability and poor control effect of DRL caused by inertia delay of the PEMEC temperature control system, multi‐empirical pool probability playback and Ornstein‐Uhlenbeck random process noise techniques are introduced on the basis of the traditional DRL method. Finally, the simulation and hardware‐in‐the‐loop experience results show that the proposed method outperforms other advanced methods.
Ma et al. (Wed,) studied this question.