Abstract: Aiming at the difficulties of state information interaction, insufficient feedback mechanism and redundant action exploration faced by the deep reinforcement learning algorithm of mobile robots in the navigation task of public services, PARL algorithm is proposed. First of all, we use the artificial potential field method and attention mechanism to design a potential field attention network. Then we use artificial potential field theory to construct a new potential field reward function. Finally, we propose a reverse approximation model. The model combines the space division method of the potential field reward function to improve the action space. The experimental results show that the use of the mobile robot driven by the PARL algorithm improves the efficiency of autonomous learning. Compared with SARL, CADRL, DRCA algorithms, the average navigation success rate and safety rate are 100% and 98.2%, respectively. The average navigation time is shortened by 0.14～1.11 s and the navigation action is more robust.