A deep reinforcement learning (DRL) based approach is proposed for automatic interpretation of well-testing curves. Based on a deep deterministic policy gradient (DDPG) algorithm, the proposed DRL approach is successfully applied to automatic matching of four different types of well-testing curves. To improve training efficiency, a surrogate well-testing model based on LSTM neural network was established. With episodic training, through interaction with the surrogate model the agent converged finally to an optimal curve matching policy on different well-testing models. It shows that the average relative error of the curve parameter interpretation is 5.51%. Additionally, the proposed DRL approach has a high calculation speed, and the average computing time is 0.27 seconds. In case study applications, the proposed method achieved an average relative error of 4.32% in parameter interpretation, which shows reliability of the method.