To improve the I/O service efficiency of RAID and optimize the I/O performance of parallel applications,the paper presents an intelligent I/O scheduling algorithm,RLscheduler,in the RAID controllers based on reinforcement learning.RLscheduler utilizes the Qlearning strategy to implement a selfcontrol and selfoptimization scheduler.The algorithm leverages the scheduling equity,disk seeking time and the I/O access efficiency of the MPI applications.Furthermore,the proposed interleaving organization of multiple Qtables improves the efficiency of the Qtable updating.The experimental results show that,on a largescale parallel system with multiple parallel applications,RLscheduler shortens the average I/O waiting time of parallel applications considerably,thus increases the practical I/O throughput of largescale parallel systems.