论文标题
一项关于航空应用中强化学习的调查
A Survey on Reinforcement Learning in Aviation Applications
论文作者
论文摘要
与基于模型的控制和优化方法相比，增强学习（RL）提供了一个基于数据驱动的，基于学习的框架，以制定和解决顺序决策问题。由于航空业的数据可用性和计算能力，RL框架已变得有希望。许多基于航空的应用程序可以被制定或视为顺序决策问题。其中一些是离线计划问题，而另一些则需要在线解决，并且至关重要。在本调查文件中，我们首先描述标准RL配方和解决方案。然后，我们调查了现有的基于RL的航空应用程序的景观。最后，我们总结了论文，确定技术差距，并提出了RL研究中RL研究的未来方向。
Compared with model-based control and optimization methods, reinforcement learning (RL) provides a data-driven, learning-based framework to formulate and solve sequential decision-making problems. The RL framework has become promising due to largely improved data availability and computing power in the aviation industry. Many aviation-based applications can be formulated or treated as sequential decision-making problems. Some of them are offline planning problems, while others need to be solved online and are safety-critical. In this survey paper, we first describe standard RL formulations and solutions. Then we survey the landscape of existing RL-based applications in aviation. Finally, we summarize the paper, identify the technical gaps, and suggest future directions of RL research in aviation.
