Deep Reinforcement Learning for UAV Target Search and Continuous Tracking in Complex Environments with Gaussian Process Regression and Prior Policy Embedding

In recent years, unmanned aerial vehicles (UAVs) have shown substantial application value in continuous target tracking tasks in complex environments. Due to the target’s movement behavior and the complexities of the surrounding environment, the UAV is prone to losing track of the target. To tackle this issue, this paper presents a reinforcement learning (RL) approach that combines UAV target search and tracking. During the target search phase, spatial information entropy is employed to guide the UAV in avoiding redundant searches, thus enhancing information acquisition efficiency. In the event of target loss, Gaussian process regression (GPR) is employed to predict the target trajectory, thereby reducing the time needed for target re-localization. In addition, to address sample efficiency limitations in conventional RL, a Kolmogorov–Arnold networks-based deep deterministic policy gradient (KbDDPG) algorithm with prior policy embedding is proposed for controller training.Simulation results demonstrate that the proposed method outperforms traditional methods in target search and tracking tasks within complex environments. It improves the UAV’s ability to re-locate the target after loss. The proposed KbDDPG efficiently leverages prior policy, leading to accelerated convergence and enhanced performance.