arXiv AI June 4, 2026 By Mohit Prashant, Arvind Easwaran

Scenario Generation for Risk-Aware Reinforcement Learning with Probably Approximately Safe Guarantees

arXiv:2606. 04812v1 Announce Type: cross Abstract: Guaranteeing safety is critical to the deployment of reinforcement learning (RL) agents in the real-world, especially as policies learned using deep RL may demonstrate susceptibility to transition perturbations that result in unknown or unsafe behaviour.

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

arXiv AI

Jun 10

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration

arXiv:2606. 10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains.

By Kaustubh Mani, Yann Pequignot, Vincent Mai, Liam Paull

agents reinforcement-learning safety

arXiv AI

Jun 8

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

arXiv:2510. 09041v3 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has demonstrated remarkable success in developing autonomous driving policies.

By Junchao Fan, Qi Wei, Ruichen Zhang, Yang Lu, Jianhua Wang, Xiaolin Chang, Bo Ai

agents reinforcement-learning robotics benchmarks safety

arXiv AI

Jun 16

Safe Exploration via Policy Priors

arXiv:2601. 19612v3 Announce Type: replace-cross Abstract: Safe exploration is a key requirement for reinforcement learning (RL) agents to learn and adapt online, beyond controlled (e.

By Manuel Wendl, Yarden As, Manish Prajapat, Anton Pollak, Stelian Coros, Andreas Krause

agents reinforcement-learning benchmarks 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

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 Machine Learning

Jun 30

Constrained Policy Optimization with Cantelli-Bounded Value-at-Risk

arXiv:2601. 22993v4 Announce Type: replace Abstract: We introduce Canary, a risk-averse method designed to optimize Value-at-Risk (VaR) constrained reinforcement learning (RL) problems.

By Rohan Tangri, Jan-Peter Calliess

reinforcement-learning benchmarks safety

Related stories

SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration

Robust Driving Control for Autonomous Vehicles: An Intelligent General-sum Constrained Adversarial Reinforcement Learning Approach

Safe Exploration via Policy Priors

SMC-ES: Automated synthesis of formally verified control policies

Uncertainty-Aware Predictive Safety Filters for Probabilistic Neural Network Dynamics

Constrained Policy Optimization with Cantelli-Bounded Value-at-Risk