Self-Organized Model Predictive Control for Air Traffic Management

In this paper a distributed model predictive control has been proposed for air traffic management problem in which aircraft use optimization to determine their own flight trajectories. The coordination approach of Self-organized Time Division Multiple Access is used to ensure no two aircraft re-plan their trajectories simultaneously. Unlike existing distributed predictive control, which needs a pre-organized optimizing sequence, this new approach requires no central coordination. By also terminating every trajectory with a loitering circle, recursive feasibility and constraint satisfaction, especially separation, can be guaranteed

Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback

We propose a method to capture the handling abilities of fast jet pilots in a software model via reinforcement learning (RL) from human preference feedback. We use pairwise preferences over simulated flight trajectories to learn an interpretable rule-based model called a reward tree, which enables the automated scoring of trajectories alongside an explanatory rationale. We train an RL agent to execute high-quality handling behaviour by using the reward tree as the objective, and thereby generate data for iterative preference collection and further refinement of both tree and agent. Experiments with synthetic preferences show reward trees to be competitive with uninterpretable neural network reward models on quantitative and qualitative evaluations