论文标题
深度强化学习碰撞系统的奖励功能优化系统
Reward Function Optimization of a Deep Reinforcement Learning Collision Avoidance System
论文作者
论文摘要
无人飞机系统（UAS）的扩散导致空域调节当局检查这些飞机与最初为大型运输类别飞机设计的碰撞避免系统的互操作性。当前授权的TCA的局限性导致联邦航空管理局（Federal Aviation Administration）委托开发一种新解决方案，即机载碰撞避免系统X（ACAS X），旨在为包括UAS在内的多个飞机平台具有碰撞避免能力。尽管先前的研究使用深度加固学习算法（DRL）进行避免碰撞，但DRL的性能不如现有解决方案。这项工作探讨了使用DRL碰撞系统使用替代优化器调整参数的好处。我们显示了替代优化器的使用导致DRL方法，该方法可以提高安全性和运行性生存能力，并为避免UAS碰撞的未来能力开发。
The proliferation of unmanned aircraft systems (UAS) has caused airspace regulation authorities to examine the interoperability of these aircraft with collision avoidance systems initially designed for large transport category aircraft. Limitations in the currently mandated TCAS led the Federal Aviation Administration to commission the development of a new solution, the Airborne Collision Avoidance System X (ACAS X), designed to enable a collision avoidance capability for multiple aircraft platforms, including UAS. While prior research explored using deep reinforcement learning algorithms (DRL) for collision avoidance, DRL did not perform as well as existing solutions. This work explores the benefits of using a DRL collision avoidance system whose parameters are tuned using a surrogate optimizer. We show the use of a surrogate optimizer leads to DRL approach that can increase safety and operational viability and support future capability development for UAS collision avoidance.
