@inproceedings{211b6265db1b47bc8034a085339163b8,
title = "Reinforcement Learning Optimal Control with A Model Identifier Based on Online Spectral Adaptive Law",
abstract = "This paper proposes an reinforcement learning (RL) optimal control method based on an online spectral adaptive law (SPAL) model identifier. Distinguished from previous studies, to enhance the generalization capability of the neural network (NN) and the robustness of the system, the proposed method employs a rectified linear unit (ReLU) activated NN in the online model identifier, and utilizes a projection algorithm with a spectral adaptive law to update the NN weights online. By constructing a Lyapunov function incorporating the spectral norm of the NN weights, it is proven that the weights can converge to their true values.",
keywords = "Optimal control, Reinforcement learning, Spectral adaptive law",
author = "Yuteng Tian and Xuemei Ren and Yongfeng Lv and Dongdong Zheng",
note = "Publisher Copyright: {\textcopyright} 2025 Technical Committee on Control Theory, Chinese Association of Automation.; 44th Chinese Control Conference, CCC 2025 ; Conference date: 28-07-2025 Through 30-07-2025",
year = "2025",
doi = "10.23919/CCC64809.2025.11178818",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "2711--2716",
editor = "Jian Sun and Hongpeng Yin",
booktitle = "Proceedings of the 44th Chinese Control Conference, CCC 2025",
address = "United States",
}