  This paper explores the method of achieving autonomous navigation of unmanned
vehicles through Deep Reinforcement Learning (DRL). The focus is on using the
Deep Deterministic Policy Gradient (DDPG) algorithm to address issues in
high-dimensional continuous action spaces. The paper details the model of a
Ackermann robot and the structure and application of the DDPG algorithm.
Experiments were conducted in a simulation environment to verify the
feasibility of the improved algorithm. The results demonstrate that the DDPG
algorithm outperforms traditional Deep Q-Network (DQN) and Double Deep
Q-Network (DDQN) algorithms in path planning tasks.
