… on the trajectory design for autonomous UAVs. In addition, the … We believe this work is
the first to address multi-UAV path … While some challenges to real-world deep reinforcement …

… Path planning remains a challenge for Unmanned Aerial Vehicles (UAVs) in dynamic … In
this paper, we have proposed a Deep Reinforcement Learning (DRL) approach for UAV path …

… deep deterministic policy gradient (DDPG) algorithm for path following control problem of
UAV. A specic reward function is designed for minimizing the cross-track error of the path …

… unmanned aerial vehicle (UAV)-mounted mobile edge computing network, where the UAV
… To ensure the quality-of-service (QoS) of each TU, the UAV with limited energy dynamically …

… This article develops a deep reinforcement learning algorithm for the UAV path planning
in the complex and dynamic environment, and proposes two methods to speed up algorithm …

… novel explainable deep neural network-based path planner … ) and the path planner is trained
using Deep Reinforcement … are conducted to illustrate that our path planner trained in the …

… UAV ground target tracking under obstacle environments using deep reinforcement learning,
and an improved deep … constructed to guide the behavior of UAV to achieve target tracking, …

… UAVs’ locations. Therefore, to solve this game, we propose a deep RL ESN-based algorithm,
using which the UAVs … optimal paths as well as the allocation of their resources along their …

… of machine learning, deep reinforcement learning (DRL) as a … In DRL, UAVs act as agents
to learn navigation strategies … a 3D path optimization problem, in which the UAV needs to learn …

… for autonomous unmanned aerial vehicles (UAVs) are … autonomous UAV path planning based
on deep reinforcement learning … compare coverage path planning (CPP), where the UAV’s …