63 citations · 81 across the 42 of their papers we have counts for
6 papers · 1 filter
Hierarchical Upper Confidence Bounds for Constrained Online Learning
Ali Baheri
The multi-armed bandit (MAB) problem is a foundational framework in sequential decision-making under uncertainty, extensively studied for its applications in areas such as clinical…
Optimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach
Zahra Shahrooei, Mykel J. Kochenderfer, Ali Baheri
Testing controllers in safety-critical systems is vital for ensuring their safety and preventing failures. In this paper, we address the falsification problem within learning-based…
Optimal Transport-Assisted Risk-Sensitive Q-Learning
Zahra Shahrooei, Ali Baheri
The primary goal of reinforcement learning is to develop decision-making policies that prioritize optimal performance without considering risk or safety. In contrast, safe reinforc…
Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning
Lunet Yifru, Ali Baheri
Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades. Yet, deploying RL policies in real-world scenarios presents…
BEACON: A Bayesian Evolutionary Approach for Counterexample Generation of Control Systems
Joshua Yancosek, Ali Baheri
The rigorous safety verification of control systems in critical applications is essential, given their increasing complexity and integration into everyday life. Simulation-based fa…
The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning
Ali Baheri, Mykel J. Kochenderfer
This paper explores the integration of optimal transport (OT) theory with multi-agent reinforcement learning (MARL). This integration uses OT to handle distributions and transporta…