Search Results for author:
Found 15 papers, 1 papers with code
BEACON: A Bayesian Evolutionary Approach for Counterexample Generation of Control Systems
Simulation-based falsification approaches play a pivotal role in the safety verification of control systems, particularly within critical applications.
Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning
Reinforcement learning (RL) has revolutionized decision-making across a wide range of domains over the past few decades.
The Synergy Between Optimal Transport Theory and Multi-Agent Reinforcement Learning
This paper explores the integration of optimal transport (OT) theory with multi-agent reinforcement learning (MARL).
LLMs-augmented Contextual Bandit
Contextual bandits have emerged as a cornerstone in reinforcement learning, enabling systems to make decisions with partial feedback.
Understanding Reward Ambiguity Through Optimal Transport Theory in Inverse Reinforcement Learning
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 but also generalizes to unseen scenarios.
Risk-Aware Reinforcement Learning through Optimal Transport Theory
In the dynamic and uncertain environments where reinforcement learning (RL) operates, risk management becomes a crucial factor in ensuring reliable decision-making.
Towards Theoretical Understanding of Data-Driven Policy Refinement
This paper presents an approach for data-driven policy refinement in reinforcement learning, specifically designed for safety-critical applications.
Joint Falsification and Fidelity Settings Optimization for Validation of Safety-Critical Systems: A Theoretical Analysis
We propose a joint falsification and fidelity optimization framework for safety validation of autonomous systems.
Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning
We showcased our framework in grid-world environments, successfully identifying both acceptable safety constraints and RL policies while demonstrating the effectiveness of our theorems in practice.
Falsification of Learning-Based Controllers through Multi-Fidelity Bayesian Optimization
Simulation-based falsification is a practical testing method to increase confidence that the system will meet safety requirements.
A Survey on Reinforcement Learning in Aviation Applications
Then we survey the landscape of existing RL-based applications in aviation.
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes
A promising approach for scalable Gaussian processes (GPs) is the Karhunen-Lo\`eve (KL) decomposition, in which the GP kernel is represented by a set of basis functions which are the eigenfunctions of the kernel operator.
A Verification Framework for Certifying Learning-Based Safety-Critical Aviation Systems
We present a safety verification framework for design-time and run-time assurance of learning-based components in aviation systems.
Black-Box Safety Validation of Autonomous Systems: A Multi-Fidelity Reinforcement Learning Approach
To address this challenge, simulation-based safety validation is employed to test the complex system.
Safe Reinforcement Learning with Mixture Density Network: A Case Study in Autonomous Highway Driving
This paper presents a safe reinforcement learning system for automated driving that benefits from multimodal future trajectory predictions.
