Safe RL

RL: Reinforcement Learning

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19 papers in the last four weeks, up 138% on the four weeks before. 0.2% of all new papers.

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Safe Learning Control with Optimality and Stability Guarantees
Jan 26, 2025Xinyang Wang, Hongwei Zhang, Shimin Wang +2Control Barrier Functions Robust Control
Shenzhen Key Lab for Advanced Motion Control and Modern Automation Equipments, School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China · School of Data Science, Lingnan University, Hong Kong · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, with M3S, SMART, and with MIT CSAIL +1
Constrained Reinforcement Learning for Safe Heat Pump Control
Sep 29, 2024Baohe Zhang, Lilli Frison, Thomas Brox +1RL Control Constrained RL
Faculty of Computer Science, University of Freiburg, Germany · Systems Control and Optimization Laboratory, Department of Microsystems Engineering, University of Freiburg, Germany
Safe Learning Under Irreversible Dynamics via Asking for Help
Date pendingBenjamin Plaut, Juan Liévano-Karim, Hanlin Zhu +1Regret Minimization in RL Reinforcement Learning
Division of Computer Science University of California Berkeley, CA 94720-1776, USA

Jan 26, 2025, eess.SYJ/K move · Enter open · S save

Safe Learning Control with Optimality and Stability Guarantees

Xinyang Wang, Hongwei Zhang, Shimin Wang, Wei Xiao+1

Shenzhen Key Lab for Advanced Motion Control and Modern Automation Equipments, School of Intelligence Science and Engineering, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China · School of Data Science, Lingnan University, Hong Kong · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, with M3S, SMART, and with MIT CSAIL+1

Control Barrier Functions Robust Control Safe RL Optimal Control Model-Based RL

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Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance. How to guarantee both safety and performance in learning-based control problems is an interesting yet challenging issue. This paper aims to enhance system performance with a safety guarantee by solving reinforcement learning (RL)-based optimal control problems for nonlinear systems subject to high-relative-degree state constraints and unknown time-varying disturbance/actuator faults. A new type of control barrier functions (CBFs), termed high-order reciprocal-based control barrier function, is proposed to handle high-relative-degree constraints, which extends the design of CBFs to enforce robust safety without knowing the disturbance bound. The concept of gradient similarity is proposed to quantify the relationship between safety and performance. Finally, gradient manipulation and adaptive mechanisms are introduced in the model-based safe RL framework to enhance the performance with a safety guarantee. Two simulation examples illustrate the efficacy of the proposed algorithms.

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Latest papers 153

Safe Learning Control with Optimality and Stability Guarantees

Constrained Reinforcement Learning for Safe Heat Pump Control

Safe Learning Under Irreversible Dynamics via Asking for Help