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 …

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 …

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 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 …

Maneuvering target tracking means that UAV observes the target through sensors, follows
the target and maintains the tracking range with the target. Aiming at the target tracking …

Active tracking control is essential for UAVs to perform autonomous operations in GPS-
denied environments. In the active tracking task, UAVs take high-dimensional raw images as …

Several approaches have applied Deep Reinforcement Learning (DRL) to Unmanned Aerial
Vehicles (UAVs) to do autonomous object tracking. These methods, however, are resource …

Uncertainty of target motion, limited perception ability of onboard cameras, and constrained
control have brought new challenges to unmanned aerial vehicle (UAV) dynamic target …