Unmanned aerial vehicles (UAVs) have been widely used in urban target-tracking tasks,
where long-term tracking of evasive targets is of great significance for public safety …

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 …

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 …

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 …

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 …

This study proposes a UAV target tracking method using reinforcement learning algorithm
combined with Gate Recurrent Unit (GRU) to promote UAV target tracking and visual …

Maintaining rapid and prolonged standoff target tracking for networked unmanned aerial
vehicles (UAVs) is challenging, as existing methods fail to improve tracking performance …

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 …

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 …

Nowadays, deep reinforcement learning (DRL) has made remarkable achievements in
unmanned aerial vehicle (UAV) target following. However, current DRL-based solutions only …