Robust Guidance Policies Through Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) guidance systems must operate reliably under significant uncertainties, such as sensor noise, target maneuvers, and environmental disturbances. Traditional guidance methods like proportional navigation (PN), while computationally efficient, often struggle to maintain performance under such challenging conditions. To overcome these limitations, this study proposes a robust UAV guidance framework based on deep reinforcement learning (DRL), specifically utilizing the soft actor–critic (SAC) algorithm. The UAV–target tracking problem is formulated as the Markov decision process (MDP) for both two-dimensional (2D) and three-dimensional (3D) scenarios. A deep neural network policy is trained in noisy environments to generate acceleration commands that minimize the zero-effort miss (ZEM). Extensive numerical simulations conducted using the OpenAI Gym validate effectiveness of the proposed method under previously unseen initial conditions and increased noise levels. The results demonstrate that the SAC-based policy achieves higher tracking success rates than the PN, particularly under strict terminal conditions and observation noise.