The project implements deep reinforcement learning algorithms for UAV autonomous navigation in both static and dynamic environments. In static environments it combines multi-agent reinforcement learning (MADDPG) with artificial potential field algorithms. In dynamic environments it applies PPO, TD3, DDPG, and SAC with disturbed flow field algorithms. It also includes traditional path planning methods (A*, RRT, ant colony) implemented in MATLAB and C++ for comparison.