Deterministic Policy Gradient for Optimizing Uav Trajectories: A Comparative Reward Function Study

Unmanned aerial vehicles (UAVs) are becoming indispensable for a variety of purposes, including surveillance and delivery, but they are still highly challenging to operate in dynamic environments. While classical control systems often perform well in stable situations, real-world issues such as wind gusts, sensor noise, and system delays can compromise the accuracy of UAV trajectory control. To get over these challenges and enhance UAV trajectory control in unpredictable situations, this paper proposes a complex technique that makes use of Deep Deterministic Policy Gradient (DDPG) reinforcement learning. Through real-time flight path optimization and ongoing adjustment, the technology allows UAVs to effectively respond to dynamic environmental disturbances. Through reinforcement learning, the UAV system learns from each flying situation, progressively increasing its ability to manage difficult situations independently. With the help of the proposed method, the UAVs can alter their course in real time, maintaining accurate and stable flight paths even when there is turbulence. Experimental evidence indicates that despite external disturbances, UAVs exhibit enhanced stability and decrease deviations from their intended trajectories, leading to significant improvements in trajectory tracking. This study improves the subject of UAV autonomy by showcasing how reinforcement learning can be used to develop more intelligent and adaptive systems. With a broad range of applications across sectors, the results indicate that DDPG-based control systems can provide a workable way to improve the autonomy, robustness, and dependability of UAVs in challenging real-world operating scenarios..