In order to solve the problem of slow convergence speed of reinforcement learning tasks with large state space, a priority-based parallel reinforcement learning task scheduling strategy was proposed. Firstly, the convergence of Q-learning in asynchronous parallel computing mode was proved. Secondly, complex problems were divided according to state spaces, then sub-problems and computing nodes were matched at the scheduling center, and each computing node completed the reinforcement learning tasks of sub-problems and gave feedback to the center to realize parallel reinforcement learning in the computer cluster. Finally, the experimental environment was built based on CloudSim, the parameters such as optimal step length, discount rate and sub-problem size were solved and the performance of the proposed strategy with different computing nodes was proved by solving practical problems. With 64 computing nodes, compared with round-robin scheduling and random scheduling, the efficiency of the proposed strategy was improved by 61% and 86% respectively. Experimental results show that the proposed scheduling strategy can effectively speed up the convergence under parallel computing, and it takes about 1.6×10 ⁵ s to get the optimal strategy for the control probelm with 1 million state space.