Hugging Face Trending Papers

Safe Reinforcement Learning using Ideas from Model Predictive Control

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 Machine Learning

Jul 9

Safe Reinforcement Learning using Ideas from Model Predictive Control

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

agents reinforcement-learning robotics safety

arXiv AI

Jul 17

SMC-ES: Automated synthesis of formally verified control policies

arXiv:2607. 15003v1 Announce Type: new Abstract: The deployment of autonomous cyber-physical systems in safety-critical environments requires closed-loop control strategies (i.

By Riccardo Curcio, Toni Mancini, Enrico Tronci

agents reinforcement-learning safety

arXiv Machine Learning

Jul 17

SafeOR-Gym: A Benchmark Suite for Safe Reinforcement Learning Algorithms on Practical Operations Research Problems

arXiv:2506. 02255v2 Announce Type: replace Abstract: Most existing safe reinforcement learning (RL) benchmarks focus on robotics and control tasks, offering limited relevance to high-stakes domains that involve structured constraints, mixed-integer decisions, and industrial complexity.

By Asha Ramanujam (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Adam Elyoumi (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Hao Chen (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Sai Madhukiran Kompalli (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Akshdeep Singh Ahluwalia (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Shraman Pal (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN), Dimitri J. Papageorgiou (Energy Sciences, ExxonMobil Technology and Engineering Company, Annandale, NJ), Can Li (Davidson School of Chemical Engineering, Purdue University, West Lafayette, IN)

reinforcement-learning robotics benchmarks

Hugging Face Trending Papers

Jul 14

Directional Constraints for Efficient Exploration in Safe Reinforcement Learning

Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation. However, real-world deployment in open-ended environments requires strong safety guarantees to prevent dangerous or harmful behaviors.

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jul 7

Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System

arXiv:2607. 03125v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) offers powerful control for industrial cyber-physical systems (ICPSs), but its "black-box" exploration risks violating strict hardware safety limits.

By Georg Sch\"afer, Jakob Rehrl, Stefan Huber

reinforcement-learning safety

arXiv Machine Learning

Jul 1

Safe Online Learning via Smooth Safety-Structured Policy Composition

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

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 15

Directional Constraints for Efficient Exploration in Safe Reinforcement Learning

arXiv:2607. 12784v1 Announce Type: cross Abstract: Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation.

By Paolo Magliano, Puze Liu, Jan Peters, Davide Tateo, Raffaello Camoriano

agents reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Aug 12

Topological Feasibility Guarantees for Differentiable Predictive Control

arXiv:2608. 10332v1 Announce Type: cross Abstract: Differentiable predictive control (DPC), a self-supervised learning approach for approximating explicit model predictive control (MPC) policies, offers significant computational advantages over online optimization-based MPC.

By Guangyu Wu, J\'an Drgo\v{n}a

reinforcement-learning safety

arXiv Machine Learning

Jun 15

Provably Safe, Yet Scalable Reinforcement Learning

arXiv:2606. 14536v1 Announce Type: new Abstract: Safe reinforcement learning (RL) aims to learn policies that optimize rewards while satisfying constraints.

By Kai S. Yun, Zeyang Li, Navid Azizan

reinforcement-learning robotics safety

arXiv Machine Learning

Aug 11

Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics

arXiv:2604. 26836v3 Announce Type: replace Abstract: Predictive safety filters (PSFs) leverage model predictive control to enforce constraint satisfaction during deep reinforcement learning (RL) exploration, yet their reliance on first-principles models or Gaussian processes limits scalability and broader applicability.

By Bernd Frauenknecht, Lukas Kesper, Daniel Mayfrank, Henrik Hose, Sebastian Trimpe

reinforcement-learning benchmarks safety

arXiv AI

Jun 24

Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control

arXiv:2606. 24010v1 Announce Type: new Abstract: Multi-agent systems are widely used in safety-critical applications that require coordinated behavior under strict safety constraints.

By Zihao Guo, Jianing Zhao, Ling Li, Hao Liang, Giuseppe Loianno, Yali Du

agents reinforcement-learning safety

arXiv AI

Jul 3

Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation

arXiv:2607. 01794v1 Announce Type: cross Abstract: With the rapid development of autonomous aerial systems, Unmanned Aerial Vehicles (UAVs) are increasingly deployed in applications such as inspection, environmental monitoring, and rescue, creating growing demand for reliable autonomous navigation.

By Shenghui Zhang, YuXuan Gao, Songwei Zhao, Jifeng Hu, Zijing Zhang, Hechang Chen

agents reinforcement-learning safety

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