当前位置: X-MOL 学术IEEE Trans. Neural Netw. Learn. Syst. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Event-Triggered Multigradient Recursive Reinforcement Learning Tracking Control for Multiagent Systems
IEEE Transactions on Neural Networks and Learning Systems ( IF 10.4 ) Pub Date : 2021-07-16 , DOI: 10.1109/tnnls.2021.3094901
Weiwei Bai , Tieshan Li , Yue Long , C. L. Philip Chen

In this article, the tracking control problem of event-triggered multigradient recursive reinforcement learning is investigated for nonlinear multiagent systems (MASs). Attention is focused on the distributed reinforcement learning approach for MASs. The critic neural network (NN) is applied to estimate the long-term strategic utility function, and the actor NN is designed to approximate the uncertain dynamics in MASs. The multigradient recursive (MGR) strategy is tailored to learn the weight vector in NN, which eliminates the local optimal problem inherent in gradient descent method and decreases the dependence of initial value. Furthermore, reinforcement learning and event-triggered mechanism can improve the energy conservation of MASs by decreasing the amplitude of the controller signal and the controller update frequency, respectively. It is proved that all signals in MASs are semiglobal uniformly ultimately bounded (SGUUB) according to the Lyapunov theory. Simulation results are given to demonstrate the effectiveness of the proposed strategy.

中文翻译:

多智能体系统的事件触发多梯度递归强化学习跟踪控制

在本文中,针对非线性多智能体系统 (MAS) 研究了事件触发多梯度递归强化学习的跟踪控制问题。注意力集中在 MAS 的分布式强化学习方法上。批评家神经网络 (NN) 用于估计长期战略效用函数,演员 NN 用于近似 MAS 中的不确定动态。多梯度递归(MGR)策略专为学习神经网络中的权重向量而设计,消除了梯度下降法固有的局部最优问题,降低了对初始值的依赖。此外,强化学习和事件触发机制可以分别通过降低控制器信号的幅度和控制器更新频率来提高 MAS 的能量守恒。根据Lyapunov理论证明了MASs中的所有信号都是半全局一致最终有界的(SGUUB)。仿真结果证明了所提策略的有效性。
更新日期:2021-07-16
down
wechat
bug