论文标题
使用深厚的强化学习，在城市环境中的无人机目标跟踪
UAV Target Tracking in Urban Environments Using Deep Reinforcement Learning
论文作者
论文摘要
由于视野有限，障碍物的可见性和不确定的目标运动，使用无人机在城市环境中持续的目标跟踪是一项艰巨的任务。车辆需要以3D智能计划，以使目标可见性最大化。在本文中，我们介绍了DQN（TF-DQN）之后的目标，这是一种基于深层Q-NETWORKS的深入增强学习技术，其课程培训框架使无人机在存在障碍和目标运动不确定性的情况下持续跟踪目标。该算法通过定性和定量进行多个模拟实验评估。结果表明，无人机在不同的环境中持续跟踪目标，同时避免在训练有素的环境和看不见的环境上遇到障碍。
Persistent target tracking in urban environments using UAV is a difficult task due to the limited field of view, visibility obstruction from obstacles and uncertain target motion. The vehicle needs to plan intelligently in 3D such that the target visibility is maximized. In this paper, we introduce Target Following DQN (TF-DQN), a deep reinforcement learning technique based on Deep Q-Networks with a curriculum training framework for the UAV to persistently track the target in the presence of obstacles and target motion uncertainty. The algorithm is evaluated through several simulation experiments qualitatively as well as quantitatively. The results show that the UAV tracks the target persistently in diverse environments while avoiding obstacles on the trained environments as well as on unseen environments.
