arXiv AI June 15, 2026 By Ayoub Belouadah, Sylvain Kubler, Yves Le Traon

CSPO: Constraint-Sensitive Policy Optimization for Safe Reinforcement Learning

arXiv:2606. 14415v1 Announce Type: new Abstract: Safe reinforcement learning (Safe RL) aims to maximize expected return while satisfying safety constraints, typically modeled as Constrained Markov Decision Processes (CMDPs).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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Boundary-Seeking Policy Gradient for Safe Reinforcement Learning

arXiv:2608. 10204v1 Announce Type: new Abstract: Safe reinforcement learning maximizes reward subject to safety constraints.

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arXiv Machine Learning

Jul 20

Robust Peak-cost Constrained Reinforcement Learning

arXiv:2607. 15457v1 Announce Type: new Abstract: We study robust peak-cost constrained reinforcement learning (RP-CRL), where the objective is to maximize expected reward while controlling the maximum cost encountered along a trajectory.

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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.

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arXiv Machine Learning

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Utility-Constrained Policy Optimization

arXiv:2606. 14029v1 Announce Type: new Abstract: Constrained MDPs (CMDPs) are a widely adopted framework for incorporating safety into RL agents; however, the framework does not support risk-sensitive constraints.

By Mehrdad Moghimi, Bernardo Avila Pires

agents reinforcement-learning benchmarks safety

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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

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)

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