Abstract: Course scheduling is a routine and important matter in teaching activities.The traditional manual course scheduling method is time-consuming and laborious,and prone to errors,which cannot meet the needs of large-scale course scheduling.However,the classical course scheduling genetic algorithm has problems such as too fast convergence speed and the efficiency of course scheduling decreases with the increase of constraint factors.Aiming at the problems of existing course scheduling genetic algorithms,a self-learning course scheduling genetic algorithm(GA-DRL) based on deep reinforcement learning is proposed.GA-DRL algorithm uses Q-learning algorithm to realize the adaptive adjustment of cross parameter and variation parameter,and enhances the searching ability of genetic algorithm.By establishing a dynamic parameter adjustment model of Markov decision process(MDP),the state set of fitness function is analyzed,and the overall performance of the population is evaluated comprehensively.At the same time,the deep Q-network algorithm(DQN) is introduced into the scheduling problem to solve the problem of multiple population states and large amount of Q-table data.Experimental results show that GA-DRL algorithm improves accuracy and optimization ability compared with the classical course scheduling genetic algorithm and improved genetic algorithm.The proposed algorithm can also be applied to problems such as examination room arrangement,cinema seating and airline route planning.

Key words: Scheduling questions, Genetic algorithm, Q-Learning, DQN