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 recent years, deep reinforcement learning (DRL) has developed rapidly and has been
applied to multi-UAV target tracking (MTT) research. However, DRL still faces challenges in …

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 …

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 …

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 …

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 …

Tracking maneuvering target in real time autonomously and accurately in an uncertain
environment is one of the challenging missions for unmanned aerial vehicles (UAVs). In this …

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 …

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 …

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 …