本文提出了一种基于Soft Actor-Critic (SAC)的深度强化学习框架，用于双 Ackermann 驱动转向移动机器人（DASMRs）的安全精确操控。该框架结合了Hindsight Experience Replay (HER)和CrossQ技术，提高了操控效率，并有效避免障碍物。仿真结果表明，该框架能将机器人成功导航至目标位置的概率提升至97%。

arXiv:2510.10332v1 Announce Type: cross Abstract: We present a deep reinforcement learning framework based on Soft Actor-Critic (SAC) for safe and precise maneuvering of double-Ackermann-steering mobile robots (DASMRs). Unlike holonomic or simpler non-holonomic robots such as differential-drive robots, DASMRs face strong kinematic constraints that make classical planners brittle in cluttered environments. Our framework leverages the Hindsight Experience Replay (HER) and the CrossQ overlay to encourage maneuvering efficiency while avoiding obstacles. Simulation results with a heavy four-wheel-steering rover show that the learned policy can robustly reach up to 97% of target positions while avoiding obstacles. Our framework does not rely on handcrafted trajectories or expert demonstrations.