arXiv:2608. 09653v1 Announce Type: cross Abstract: Recent advances in learning-based control have enabled impressive achievements in solving complex control problems in various domains.
By Baocong Zhang, Siliang Lu, Chenyang Li
arXiv:2607. 07252v1 Announce Type: new Abstract: Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics.
By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring strict, hard safety constraints during the active learning phase.
arXiv:2606. 31320v1 Announce Type: new Abstract: Safe online reinforcement learning requires policies to respect safety constraints while maintaining smooth optimization dynamics.
By Hongpeng Cao, Liqun Zhao, Yuliang Gu, Naira Hovakimyan, Lui Sha, Marco Caccamo
arXiv:2605. 26452v2 Announce Type: replace-cross Abstract: Safe reinforcement learning (RL) for robotic systems requires policies that improve task performance while satisfying state and input constraints during both training and deployment.
By Dhruv S. Kushwaha, Zoleikha A. Biron
arXiv:2501. 15373v2 Announce Type: replace-cross Abstract: Merely pursuing performance may adversely affect safety, while a conservative policy for safe exploration will degrade the performance.
By Xinyang Wang, Hongwei Zhang, Shimin Wang, Wei Xiao, Martin Guay