.png){kind=icon}
返回
Safety reinforcement learning control via transfer learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2024.111714.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Reinforcement learning (RL) has emerged as a promising approach for modern control systems. However, its success in real-world applications has been limited due to the lack of safety guarantees. To address this issue, the authors present a novel transfer learning framework that facilitates policy training in a non-dangerous environment, followed by transfer of the trained policy to the original dangerous environment. The transferred policy is theoretically proven to stabilize the original system while maintaining safety. Additionally, we propose an uncertainty learning algorithm incorporated in RL that overcomes natural data cascading and data evolution problems in RL to enhance learning accuracy. The transfer learning framework avoids trial-and-error in unsafe environments, ensuring not only after-learning safety but, more importantly, addressing the challenging problem of safe exploration during learning. Simulation results demonstrate the promise of the transfer learning framework for RL safety control on the task of vehicle lateral stability control with safety constraints. (c) 2024 Elsevier Ltd. All rights reserved.
Keyword:
Reinforcement learning control
Safety
Stability
Transfer learning
期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
机构
引用论文
Reinforcement Learning Control of a Flexible Two-Link Manipulator: An Experimental Investigation柔性两连杆机械手的强化学习控制: 实验研究
PREAI
分享收藏Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer's Disease Diagnosis Using Structural MRI使用结构MRI进行关节萎缩定位和阿尔茨海默氏病诊断的分层完全卷积网络
OAAI
OAAI分享收藏Practical tracking control of perturbed uncertain nonaffine systems with full state constraints.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏
Deception attacks on event-triggered distributed consensus estimation for nonlinear systems基于事件触发的非线性系统分布式一致性估计的欺骗攻击AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏
查看更多内容