63 citations · 81 across the 42 of their papers we have counts for
6 papers · 1 filter
LLMs-augmented Contextual Bandit
Ali Baheri, Cecilia O. Alm
Contextual bandits have emerged as a cornerstone in reinforcement learning, enabling systems to make decisions with partial feedback. However, as contexts grow in complexity, tradi…
Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning
Ali Baheri
In inverse reinforcement learning (IRL), the central objective is to infer underlying reward functions from observed expert behaviors in a way that not only explains the given data…
Risk-Aware Reinforcement Learning through Optimal Transport Theory
Ali Baheri
In the dynamic and uncertain environments where reinforcement learning (RL) operates, risk management becomes a crucial factor in ensuring reliable decision-making. Traditional RL…
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…
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…
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…