Path planning using deep reinforcement learning based on potential field in complex environment. Issue 2 (January 2021)

• Record Type: Journal Article
• Authors: Jia, Qingxuan; Yang, Maonan; Miao, Yu; Li, Xulong
• Is Part Of: Journal of physics. Volume 1748:Issue 2(2021)
• Publication Date: 2021-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1748/2/022016
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: This paper introduces a deep reinforcement learning path planning method based on potential field for complex environment. Based on the potential field model in the artificial potential field method, we define states, actions, rewards in reinforcement learning, and use Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm for optimization. By training robots in the environment, our method can effectively plan the path in a complex environment with massive obstacles, and avoid trapping in the local minimum region of the potential field.