Advanced Air Mobility (AAM) introduces a new, efficient mode of
transportation with the use of vehicle autonomy and electrified aircraft to
provide increasingly autonomous transportation between previously underserved
markets. Safe and efficient navigation of low altitude aircraft through highly
dense environments requires the integration of a multitude of complex
observations, such as surveillance, knowledge of vehicle dynamics, and weather.
The processing and reasoning on these observations pose challenges due to the
various sources of uncertainty in the information while ensuring cooperation
with a variable number of aircraft in the airspace. These challenges coupled
with the requirement to make safety-critical decisions in real-time rule out
the use of conventional separation assurance techniques. We present a
decentralized reinforcement learning framework to provide autonomous
self-separation capabilities within AAM corridors with the use of speed and
vertical maneuvers. The problem is formulated as a Markov Decision Process and
solved by developing a novel extension to the sample-efficient, off-policy soft
actor-critic (SAC) algorithm. We introduce the use of attention networks for
variable-length observation processing and a distributed computing architecture
to achieve high training sample throughput as compared to existing approaches.
A comprehensive numerical study shows that the proposed framework can ensure
safe and efficient separation of aircraft in high density, dynamic environments
with various sources of uncertainty.

Advanced Air Mobility 引入了一种新的高效的交通方式，利用车辆自动化和电动飞机在以前服务不足的市场之间提供越来越自主的交通。本文提出了一个基于去中心化强化学习框架的方法，在 AAM 走廊中利用速度和垂直机动提供自主的自主分离能力。通过引入注意力网络用于可变长度的观察处理和分布式计算架构，提高了模型的高训练样本吞吐量，并实现了在具有各种不确定性的高密度、动态环境中确保飞机安全和高效分离的目标。