基于APF-MASAC算法的多无人车路径规划研究OA

To address the path planning problem for multiple unmanned vehicles in real-world environments,this paper proposes an algorithm design scheme under the Multi-Agent Soft Actor-Critic(MASAC)framework.To en-hance the algorithm's performance,we propose three improvements.First,drawing inspiration from the Artificial Po-tential Field(APF)concept,we design a dense reward function based on potential shaping techniques to provide abundant,timely and effective feedback signals during the learning process,thereby significantly accelerating con-vergence.Second,the traditional experience replay buffer is modified by adopting a double-consecutive-frame tech-nique.This approach incorporates two consecutive observation frames as unified units into the experience replay buffer,effectively capturing environmental dynamics and improving training stability.Third,a highly realistic dynam-ic obstacle environment is constructed using the Gazebo simulation platform,which provides diverse and challenging training samples,ensuring comprehensive learning and optimization under near-real conditions.Finally,the effective-ness of the proposed APF-MASAC algorithm is validated through ablation experiments and robustness tests.