论文标题
通过环境概括的深度加固学习混合空中水下汽车的无地图导航
Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization
论文作者
论文摘要
先前的工作表明，Deep-RL可用于执行无地图导航，包括混合无人驾驶空中水下车辆（Huauvs）的中等过渡。本文介绍了基于最先进的演员批评算法的新方法，以解决Huauv的导航和中型过渡问题。我们表明，具有复发性神经网络的双重评论家Deep-RL使用仅范围数据和相对定位来改善Huauvs的导航性能。我们的深-RL方法通过通过不同的模拟场景对学习的坚实概括，实现了更好的导航和过渡能力，表现优于先前的方法。
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.
