  In this paper, we propose an autonomous UAV path planning framework using
deep reinforcement learning approach. The objective is to employ a self-trained
UAV as a flying mobile unit to reach spatially distributed moving or static
targets in a given three dimensional urban area. In this approach, a Deep
Deterministic Policy Gradient (DDPG) with continuous action space is designed
to train the UAV to navigate through or over the obstacles to reach its
assigned target. A customized reward function is developed to minimize the
distance separating the UAV and its destination while penalizing collisions.
Numerical simulations investigate the behavior of the UAV in learning the
environment and autonomously determining trajectories for different selected
scenarios.
