In this article, we propose a novel deep reinforcement learning (DRL) approach for
controlling multiple unmanned aerial vehicles (UAVs) with the ultimate purpose of tracking …

Past few years have witnessed the widespread adoption of unmanned aerial vehicles
(UAVs) in target tracking for regional monitor and strike. Most existing target tracking …

This study presents an innovative reinforcement-learning-based control algorithm for a
vertical take-off and landing (VTOL) aircraft under wind disturbances. In our approach, the …

This paper combines deep reinforcement learning (DRL) with meta-learning and proposes a
novel approach, named meta twin delayed deep deterministic policy gradient (Meta-TD3), to …

In this paper, we focus on the study of UAV ground target tracking under obstacle
environments using deep reinforcement learning, and an improved deep deterministic policy …

In recent years, deep reinforcement learning (DRL) has proved its great potential in multi-
agent cooperation. However, how to apply DRL to multi-target tracking (MTT) problem for …

With the advantages of easy deployment and flexible usage, unmanned aerial vehicle (UAV)
has advanced the multitarget tracking (MTT) applications. The UAV-MTT system has great …

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 …

The aspect ratio of a target changes frequently during an unmanned aerial vehicle (UAV)
tracking task, which makes the aerial tracking very challenging. Traditional trackers struggle …

Aiming at the problems of strong nonlinearity, strong coupling, and unknown interference
encountered in the flight control process of unmanned aerial vehicles (UAVs) in a complex …