DAR ‐ SAC : Dynamic Adaptive Regularization Soft Actor‐Critic

This paper proposes the dynamic adaptive regularization soft actor‐critic (DAR‐SAC) framework. The framework is based on the SAC algorithm, which is a reinforcement learning framework incorporating dynamic environment detection and adaptive optimization, to address the fixed exploration noise stiffness of the traditional SAC algorithm in dynamic environments. For the problems of fixed exploration noise rigidity, strategy oscillation aggravation and Q‐value over‐estimation in traditional SAC algorithms in dynamic environments, this paper firstly introduces a dynamic environment detection module to enable the robot to distinguish dynamic environments, and then combines the hybrid regularization mechanism and adaptive gradient trimming to accelerate convergence while suppressing abrupt changes in the strategy. In the constructed 3D point cloud simulation environment, the DAR‐SAC algorithm achieves significantly higher cumulative rewards and lower collision rates compared with SAC, SARC, DDPG, and TD3 algorithms. To further evaluate the robustness of the proposed framework against non‐stationary environments, we conduct extensive comparisons with state‐of‐the‐art variants, including SAC with adaptive entropy scheduling (SAC + AES), distributional SAC with three refinements (DSAC‐T), and SAC with delayed actor updates and replay mixing (SAC + DA + RM). The experimental results demonstrate that the proposed method exhibits superior path‐planning performance in complex static and dynamic environments.