# Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

arXiv: https://arxiv.org/abs/2407.00197 · HTML: https://mlsanity.com/paper/2407.00197/

## Key facts

- No citing papers in the map yet (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 10 of its 43 references that are in the map.
- First posted on arXiv 28 Jun 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, 10 of 10)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Soft Actor-Critic for Discrete Action Settings](https://mlsanity.com/paper/1910.07207/) (2019)
- [Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis](https://mlsanity.com/paper/1807.07814/) (2018)
- [A Survey on Reinforcement Learning in Aviation Applications](https://mlsanity.com/paper/2211.02147/) (2022)
- [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)
- [Improving Autonomous Separation Assurance through Distributed Reinforcement Learning with Attention Networks](https://mlsanity.com/paper/2308.04958/) (2023)
- [Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System](https://mlsanity.com/paper/2212.00855/) (2022)
- [Towards a Standardized Reinforcement Learning Framework for AAM Contingency Management](https://mlsanity.com/paper/2311.10805/) (2023)

## Abstract (arXiv)

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.