【摘要】 通过强化学习研究了异构多智能体系统的输出同步问题。根据多智能体系统的拓扑结构,定义一个具有邻居控制输入的性能指标和价值函数。为克服已有控制方法需要系统模型的弊端,提出一个基于系统数据的强化学习算法,使输出同步控制器也可以被应用于模型未知的情况。此外,通过调节价值函数中的权重矩阵,可以减少每个智能体的控制成本。最后,通过一个仿真示例验证了该方法的有效性和定义的价值函数的优越性。更多还原

【Abstract】 The output synchronization of heterogeneous multi-agent system was studied by reinforcement learning. According to the topology of multi-agent system, the performance index and value function with neighbor control input were defined. To overcome the disadvantage of existing control methods that require system model, a reinforcement learning algorithm based on system data was proposed. Hence, the output synchronization controller can also be applied when the system model was unknown. In addition, by adjusting the weight matrix in value function, the control cost of each agent can be reduced. Finally, a simulation example was given to illustrate the effectiveness of proposed method and the superiority of defined value function.更多还原