基于递阶强化学习的多智能体AGV 调度系统

递阶强化学习是解决状态空间庞大的复杂系统智能体决策的有效方法。具有离散动态特性的AGV调度系统需要实时动态的调度方法，而具有MaxQ递阶强化学习能力的多智能体通过高效的强化学习方法和协作，可以实现AGV的实时调度。仿真实验证明了这种方法的有效性。

Multiagent AGV dispatching system based on hierarchical reinforcement learning

Hierarchical reinforcement learning is an effective method of solving decision problems for complex systems with enormous number of states. AGV dispatching system needs dynamic dispatching rules because of its discrete and dynamic properties. Multiagent with the capacity of Max Q hierarchical reinforcement learning is implemented in real time AGV dispatching by high performance learning and cooperation. The simulation testifies the efficiency of this method.