AGVs dispatching, one of the hot problems in FMS, has attracted widespread interest in recent years. It is hard to dynamically schedule AGVs with pre designed rule because of the uncertainty and dynamic nature of AGVs dispatching progress, so the AGVs system in this paper is treated as a cooperative learning multiagent system, in which each agent adopts multilevel decision method, which includes two level decisions: the option level and the action level. On the option level, an agent learns a policy to execute a subtask with the best response to the other AGVs’ current options. On the action level, an agent learns an optimal policy of actions for achieving his planned option. The method is applied to a AGVs’ dispatching simulation, and the performance of the AGVs system based on this method is verified. AGVs dispatching, one of the hot problems in FMS, has prompted widespread interest in recent years. It is hard to dynamically schedule AGVs with pre designed rule because of the uncertainty and dynamic nature of AGVs dispatching progress, so the AGVs system in this paper is treated as a cooperative learning multiagent system, which includes two level decisions: the option level and the action level. On the option level, an agent learns a policy to execute a subtask with the best response to the other AGVs ’current options. On the action level, an agent learns an optimal policy of actions for achieving his planned option. The method is applied to a AGVs’ dispatching simulation, and the performance of the AGVs system based on this method is verified .