Multi-object trajectory planning method based on improved MADDPG

To address the issues of low exploration efficiency, value estimation bias, and insufficient training stability in the traditional multi-agent deep deterministic policy gradient (MADDPG) algorithm for multi- nmanned aerial vehicle（UAV）trajectory planning, this paper proposes an improved MADDPG algorithm. To preserve policy diversity while improving convergence stability, the suggested approach combines an exponentially decaying exploration noise strategy with the fundamental mechanisms of the twin delayed deep deterministic policy gradient (TD3), such as a dual-critic network, delayed policy updates, and target policy smoothing. Furthermore, tailored state and action spaces are designed for multi-UAV cooperative trajectory planning, along with a dense reward function to ensure efficient and stable path generation. A three-dimensional static simulation environment is constructed to train and comparatively evaluate the proposed improved MADDPG method against the traditional MADDPG. Experimental results demonstrate that the proposed improved MADDPG algorithm achieves rapid convergence and stable planning performance under various starting/ending positions and obstacle distributions. It validates its efficacy and robustness for cooperative multi-UAV trajectory planning in complicated airspace scenarios by achieving notable gains in path efficiency, task completion rate, and cooperative control capability when compared to the old technique.