# Towards a Standardized Reinforcement Learning Framework for AAM Contingency Management

arXiv: https://arxiv.org/abs/2311.10805 · HTML: https://mlsanity.com/paper/2311.10805/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 17 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Hindsight Experience Replay](https://mlsanity.com/paper/1707.01495/) (2017)
- [Distributional Reinforcement Learning with Quantile Regression](https://mlsanity.com/paper/1710.10044/) (2017)
- [Safety Enhancement for Deep Reinforcement Learning in Autonomous Separation Assurance](https://mlsanity.com/paper/2105.02331/) (2021)
- [Optimizing Collision Avoidance in Dense Airspace using Deep Reinforcement Learning](https://mlsanity.com/paper/1912.10146/) (2019)
- [Collision Risk and Operational Impact of Speed Change Advisories as Aircraft Collision Avoidance Maneuvers](https://mlsanity.com/paper/2204.14250/) (2022)
- [AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility](https://mlsanity.com/paper/2206.04513/) (2022)

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

- [Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility](https://mlsanity.com/paper/2407.00197/) (2024)

## Abstract (arXiv)

Advanced Air Mobility (AAM) is the next generation of air transportation that includes new entrants such as electric vertical takeoff and landing (eVTOL) aircraft, increasingly autonomous flight operations, and small UAS package delivery. With these new vehicles and operational concepts comes a desire to increase densities far beyond what occurs today in and around urban areas, to utilize new battery technology, and to move toward more autonomously-piloted aircraft. To achieve these goals, it becomes essential to introduce new safety management system capabilities that can rapidly assess risk as it evolves across a span of complex hazards and, if necessary, mitigate risk by executing appropriate contingencies via supervised or automated decision-making during flights. Recently, reinforcement learning has shown promise for real-time decision making across a wide variety of applications including contingency management. In this work, we formulate the contingency management problem as a Markov Decision Process (MDP) and integrate the contingency management MDP into the AAM-Gym simulation framework. This enables rapid prototyping of reinforcement learning algorithms and evaluation of existing systems, thus providing a community benchmark for future algorithm development. We report baseline statistical information for the environment and provide example performance metrics.