.png){kind=icon}
返回
Input-Output Data-Based Output Antisynchronization Control of Multiagent Systems Using Reinforcement Learning Approach
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2021.3050768.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article investigates an output antisynchronization problem of multiagent systems by using an input-output data-based reinforcement learning approach. Till now, most of the existing results on antisynchronization problems required full-state information and exact system dynamics in the controller design, which is always invalid in practical scenarios. To address this issue, a new system representation is constructed by using just the available input/output data from the multiagent system. Then, a novel value iteration algorithm is proposed to compute the optimal control laws for the agents; moreover, a convergence analysis is presented for the proposed algorithm. In the implementation of the data-based controllers, an actor-critic network structure is established to learn the optimal control laws without the requirement of information of the agent dynamics. An incremental weight updating rule is proposed to improve the learning performance. Finally, simulation results are presented to demonstrate the effectiveness of the proposed antisynchronization control strategy.
Keyword:
Multi-agent systems
Informatics
Synchronization
Artificial neural networks
Optimal control
Network topology
Heuristic algorithms
Incremental actor-critic (AC) network
input-output data
optimal antisynchronization
partially observable multiagent systems
reinforcement learning (RL)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
机构
引用论文
.png){kind=icon}
Optimized Formation Control Using Simplified Reinforcement Learning for a Class of Multiagent Systems With Unknown Dynamics使用简化的强化学习对一类具有未知动力学的多智能体系统进行优化的编队控制
PREAI
分享收藏
查看更多内容