# Graph Learning-based Fleet Scheduling for Urban Air Mobility under Operational Constraints, Varying Demand & Uncertainties

arXiv: https://arxiv.org/abs/2401.04851 · HTML: https://mlsanity.com/paper/2401.04851/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 9 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search](https://mlsanity.com/paper/1810.10659/) (2018)
- [Revised Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks](https://mlsanity.com/paper/1706.07450/) (2017)
- [Graph Capsule Convolutional Neural Networks](https://mlsanity.com/paper/1805.08090/) (2018)
- [Learning the Multiple Traveling Salesmen Problem with Permutation Invariant Pooling Networks](https://mlsanity.com/paper/1803.09621/) (2018)
- [Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks](https://mlsanity.com/paper/2011.01119/) (2020)
- [Multi-Agent Routing Value Iteration Network](https://mlsanity.com/paper/2007.05096/) (2020)
- [Learning Scalable Policies over Graphs for Multi-Robot Task Allocation using Capsule Attention Networks](https://mlsanity.com/paper/2205.03321/) (2022)
- [Fast Decision Support for Air Traffic Management at Urban Air Mobility Vertiports using Graph Learning](https://mlsanity.com/paper/2308.09075/) (2023)
- [Graph Attention Multi-Agent Fleet Autonomy for Advanced Air Mobility](https://mlsanity.com/paper/2302.07337/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Graph-based Adversarial Imitation Learning Framework for Reliable & Realtime Fleet Scheduling in Urban Air Mobility](https://mlsanity.com/paper/2407.12113/) (2024)

## Abstract (arXiv)

This paper develops a graph reinforcement learning approach to online planning of the schedule and destinations of electric aircraft that comprise an urban air mobility (UAM) fleet operating across multiple vertiports. This fleet scheduling problem is formulated to consider time-varying demand, constraints related to vertiport capacity, aircraft capacity and airspace safety guidelines, uncertainties related to take-off delay, weather-induced route closures, and unanticipated aircraft downtime. Collectively, such a formulation presents greater complexity, and potentially increased realism, than in existing UAM fleet planning implementations. To address these complexities, a new policy architecture is constructed, primary components of which include: graph capsule conv-nets for encoding vertiport and aircraft-fleet states both abstracted as graphs; transformer layers encoding time series information on demand and passenger fare; and a Multi-head Attention-based decoder that uses the encoded information to compute the probability of selecting each available destination for an aircraft. Trained with Proximal Policy Optimization, this policy architecture shows significantly better performance in terms of daily averaged profits on unseen test scenarios involving 8 vertiports and 40 aircraft, when compared to a random baseline and genetic algorithm-derived optimal solutions, while being nearly 1000 times faster in execution than the latter.