A modified dueling DQN algorithm for robot path planning incorporating priority experience replay and artificial potential fields

Abstract

For the challenges of low learning efficiency, slow convergence speed and slow inference speed in robot path planning. This paper proposes an improved deep reinforcement learning algorithm for robot path planning. Firstly, the Dueling DQN network architecture is employed, combined with a priority experience replay strategy, to more effectively learn from and utilize experience data. Secondly, the mobility space of the robot is expanded, enhancing the diversity and flexibility of the action space. Additionally, in the action selection process, the Artificial Potential Field (APF) algorithm is introduced to intervene in the action selection with a certain probability, thereby accelerating the convergence process of the network. Simultaneously, the \(\varepsilon\) -greedy strategy is employed to balance exploration and exploitation, facilitating better exploration of the environment and utilization of existing knowledge. Furthermore, this paper devises novel composite reward functions that comprehensively integrate multiple reward mechanisms to enhance the convergence performance of the algorithm and the quality of path planning. Finally, the effectiveness and superiority of the proposed method are validated through detailed comparative simulations. Compared to traditional DQN algorithms, Double DQN, and Double DQN with the APF strategy, the method proposed in this paper demonstrates higher learning efficiency and faster convergence speed, enabling more effective planning of shorter paths.