The invention provides a railway locomotive operation control system hybrid task scheduling method based on reinforcement learning, and the method is an offline learning process. According to the method, first hybrid task set data during actual operation or in a simulation experiment of a locomotive operation control system, a hybrid task set is formed and regularization marking is performed on each task in the hybrid task set. Then the task set after regularization marking serves as input of a reinforcement learning system, and a reinforcement learning environment is formed. The reinforcement learning system applies a reinforcement learning algorithm, inspects scheduling objectives of the locomotive operation control system to perform an iterative learning process, a corresponding relation table corresponding to the hybrid task set is generated; and the corresponding relation table is stored in a database. The rule whose frequency of occurrence is the highest is selected from the database as the optimal rule of a current state, and a final corresponding relation table is formed. During operation of the locomotive control system, generation of a real-time scheduling sequence of hybrid tasks is guided according to the corresponding relation table, thereby realizing task scheduling.