Aiming at the problems of poor performance,low efficiency and inaccuracy to express constraints of tradi-tional scheduling approaches,a day-off scheduling approach based on reinforcement learning was proposed.In this approach,the day-off scheduling process was regarded as a Markov Decision Process(MDP),and an action mask method was utilized for expressing scheduling constraints.Deep Q-Network(DQN)was developed for learning scheduling strategies from MDP.Finally,the learned scheduling strategies were used to generate scheduling results following daily workload efficiently under constraints.Compared to traditional Genetic Algorithm(GA)methods,experimental results showed that the proposed method had less variance and was more efficient.
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