2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Towards Theoretical Understanding of Data-Driven Policy Refinement
Ali Baheri
This paper presents an approach for data-driven policy refinement in reinforcement learning, specifically designed for safety-critical applications. Our methodology leverages the s…
eess.SY2023★ 2 cited
Joint Falsification and Fidelity Settings Optimization for Validation of Safety-Critical Systems: A Theoretical Analysis
Ali Baheri, Mykel J. Kochenderfer
Safety validation is a crucial component in the development and deployment of autonomous systems, such as self-driving vehicles and robotic systems. Ensuring safe operation necessi…
eess.SY2023★ 1 cited
Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning
Lunet Yifru, Ali Baheri
In many real-world applications, safety constraints for reinforcement learning (RL) algorithms are either unknown or not explicitly defined. We propose a framework that concurrentl…