.png){kind=icon}
返回
Data-Driven Safe Policy Optimization for Black-Box Dynamical Systems With Temporal Logic Specifications
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2023.3339885.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Learning-based policy optimization methods have shown great potential for building general-purpose control systems. However, existing methods still struggle to achieve complex task objectives while ensuring policy safety during learning and execution phases for black box systems. To address these challenges, we develop data-driven safe policy optimization ((DSPO)-S-2), a novel reinforcement learning (RL)-based policy improvement method that jointly learns a control barrier function (CBF) for system safety and a linear temporal logic (LTL) guided RL algorithm for complex task objectives. Unlike many existing works that assume known system dynamics, by carefully constructing the data sets and redesigning the loss functions of (DSPO)-S-2, a provably safe CBF is learned for black-box dynamical systems, which continuously evolves for improved system safety as RL interacts with the environment. To deal with complex task objectives, we take advantage of the capability of LTL in representing the task progress and develop LTL-guided RL policy for efficient completion of various tasks with LTL objectives. Extensive numerical and experimental studies demonstrate that (DSPO)-S-2 outperforms most state-of-the-art (SOTA) baselines and can achieve over 95% safety rate and nearly 100% task completion rates. The experiment video is available at https://youtu.be/2RgaH-zcmkY.
Keyword:
Control barrier function (CBF)
linear temporal logic (LTL)
reinforcement learning (RL)
safe policy optimization
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
Control Barrier Function Based Quadratic Programs for Safety Critical Systems用于安全关键系统的基于控制障碍函数的二次程序
OAAI
OAAI分享收藏A formal methods approach to interpretable reinforcement learning for robotic planning.png)
SCIENCE ROBOTICS
SCIENCE ROBOTICSIF27.5
PREAI
分享收藏Safe reinforcement learning under temporal logic with reward design and quantum action selectionSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏
Safe Reinforcement Learning With Stability Guarantee for Motion Planning of Autonomous Vehicles
PREAI
分享收藏
查看更多内容