10 papers · 1 filter
Inverting Self-Triggered Control: Adversarial Reinforcement Learning for Sparse Denial-of-Service Attacks
Adam Haroon, Erick J. Rodríguez-Seda, Tristan Schuler +1
Self-triggered reinforcement learning control (RL-STC) learns the sparsest control schedule that preserves Lyapunov-decreasing stability under a Run-Time Assurance (RTA) override.…
Certified Safety Curation: Distribution-Free Guarantees for Safe Offline Reinforcement Learning
Adam Haroon, Cody Fleming
Safe offline reinforcement learning assumes a cost function on every transition. We ask what remains possible when safety can be judged only by comparing short clips and occasional…
Who Analyses the Analyser? Self-Validating LLM Hazard Analysis with Constitutional Meta-STPA
Samuel Tetteh, Udip Shrestha, Joshua R. Waite +1
Large language models (LLMs) are increasingly trusted to draft the artifacts of safety analysis such as, losses, hazards, Unsafe Control Actions (UCAs), and safety constraints, ins…
Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models
Samuel Tetteh, Cody Fleming
The cost signal that constrained-RL algorithms optimize against is almost always reactive: the simulator emits a non-zero cost only after a collision has begun, and the Lagrange mu…
COOPO: Cyclic Offline-Online Policy Optimization Algorithm
Qisai Liu, Zhanhong Jiang, Joshua Russell Waite +3
Offline reinforcement learning struggles with distributional shift and constrained performance due to static dataset limitations, while online RL demands prohibitive environment in…
Learning When to Act: Communication-Efficient Reinforcement Learning via Run-Time Assurance
Adam Haroon, Erick J. Rodríguez-Seda, Cody Fleming +1
Safe reinforcement learning (RL) typically asks an agent should do. We ask it needs to act, and show that a single policy can jointly learn control…