Publications (6)
Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties
Yutian Pang, Peng Zhao, Jueming Hu +1
This paper addresses aircraft delays, emphasizing their impact on safety and financial losses. To mitigate these issues, an innovative machine learning (ML)-enhanced landing schedu…
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines
Jueming Hu, Zhe Xu, Weichang Wang +3
In multi-agent reinforcement learning (MARL), it is challenging for a collection of agents to learn complex temporally extended tasks. The difficulties lie in computational complex…
Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning
Jueming Hu, Xuxi Yang, Weichang Wang +3
Obstacle avoidance for small unmanned aircraft is vital for the safety of future urban air mobility (UAM) and Unmanned Aircraft System (UAS) Traffic Management (UTM). There are man…
Reinforcement Learning With Reward Machines in Stochastic Games
Jueming Hu, Jean-Raphael Gaglione, Yanze Wang +3
We investigate multi-agent reinforcement learning for stochastic games with complex tasks, where the reward functions are non-Markovian. We utilize reward machines to incorporate h…
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning
Yutian Pang, Jueming Hu, Christopher S. Lieber +2
Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The…
Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks
Yutian Pang, Sheng Cheng, Jueming Hu +1
To evaluate the robustness gain of Bayesian neural networks on image classification tasks, we perform input perturbations, and adversarial attacks to the state-of-the-art Bayesian…