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• Apply a state-of-the-art deep reinforcement learning algorithm to the PM problem. • Formulate the PM problem as an MDP with guidance of domain knowledge. • Use a data-driven modeling method to build a fast simulator for learning. • The DRL agent learns to perform group maintenance and opportunistic maintenance. • Proposed method outperforms age-dependent policy and opportunistic policy. In the manufacturing industry, the preventive maintenance (PM) is a common practice to reduce random machine failures by replacing/repairing the aged machines or parts. The decision on when and where the preventive maintenance needs to be carried out is nontrivial due to the complex and stochastic nature of a serial production line with intermediate buffers. In order to improve the cost efficiency of the serial production lines, a deep reinforcement learning based approach is proposed to obtain PM policy. A novel modeling method for the serial production line is adopted during the learning process. A reward function is proposed based on the system production loss evaluation. The algorithm based on the Double Deep Q-Network is applied to learn the PM policy. Using the simulation study, the learning algorithm is proved effective in delivering PM policy that leads to an increased throughput and reduced cost. Interestingly, the learned policy is found to frequently conduct "group maintenance" and "opportunistic maintenance", although their concepts and rules are not provided during the learning process. This finding further demonstrates that the problem formulation, the proposed algorithm and the reward function setting in this paper are effective. [ABSTRACT FROM AUTHOR]