Previous works showed that Deep-RL can be applied to perform mapless
navigation, including the medium transition of Hybrid Unmanned Aerial
Underwater Vehicles (HUAUVs). This paper presents new approaches based on the
state-of-the-art actor-critic algorithms to address the navigation and medium
transition problems for a HUAUV. We show that a double critic Deep-RL with
Recurrent Neural Networks improves the navigation performance of HUAUVs using
solely range data and relative localization. Our Deep-RL approaches achieved
better navigation and transitioning capabilities with a solid generalization of
learning through distinct simulated scenarios, outperforming previous
approaches.

本文介绍了一种基于最新的 actor-critic 算法的新方法，以解决 HUAUV 的导航和介质转换问题。我们展示了一种双重评论家 Deep-RL 的方法，通过仅使用范围数据和相对定位来提高 HUAUV 的导航性能。我们的 Deep-RL 方法在不同模拟场景中实现了学习的坚实泛化，表现出更好的导航和转换能力，优于以前的方法。