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Found 6 papers, 2 papers with code
Machine Learning-Enhanced Aircraft Landing Scheduling under Uncertainties
This paper addresses aircraft delays, emphasizing their impact on safety and financial losses.
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning
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.
Reinforcement Learning With Reward Machines in Stochastic Games
We develop an algorithm called Q-learning with reward machines for stochastic games (QRM-SG), to learn the best-response strategy at Nash equilibrium for each agent.
Obstacle Avoidance for UAS in Continuous Action Space Using Deep Reinforcement Learning
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).
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines
Experimental results show that local information is sufficient for DGRM and agents can accomplish complex tasks with the help of RM.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks
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 neural networks, with a benchmark CNN model as reference.
