Reinforcement Learning Control for Consensus of the Leader-Follower Multi-Agent Systems

被引：0
|
作者：
Chiang, Ming-Li [1 ]
Liu, An-Sheng [1 ]
Fu, Li-Chen [1 ,2 ]
机构：
[1] Natl Taiwan Univ, Dept Elect Engn, Taipei, Taiwan
[2] NTU Ctr Artificial Intelligence & Adv Robot, Taipei, Taiwan
来源：
PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS) | 2018年
关键词：
Optimal control; Reinforcement Learning; Policy iteration; Multi-Agent System; GRAPHICAL GAMES;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper considers the optimal consensus of multi-agent systems using reinforcement learning control. The system is nonlinear and the number of agents can be large. The control objective is to design the controllers for each agent such that all the agents will be consensus to the leader agent. We use the Actor-Critic Network and the Deterministic Policy Gradient method to realize the controller. The policy iteration algorithm is discussed and many simulations are provided to validate the result.
引用
收藏
页码：1152 / 1157
页数：6
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