强化学习
计算机科学
人工智能
领域(数学)
机器人
一般化
势场
过程(计算)
移动机器人
机器学习
数学
数学分析
地球物理学
纯数学
地质学
操作系统
作者
Zheng Zhang,Xiaohan Wang,Qingrui Zhang,Tianjiang Hu
标识
DOI:10.1109/icra46639.2022.9812083
摘要
It is of great challenge, though promising, to coordinate collective robots for hunting an evader in a decentralized manner purely in light of local observations. In this paper, this challenge is addressed by a novel hybrid cooperative pursuit algorithm that combines reinforcement learning with the artificial potential field method. In the proposed algorithm, decentralized deep reinforcement learning is employed to learn cooperative pursuit policies that are adaptive to dynamic environments. The artificial potential field method is integrated into the learning process as predefined rules to improve the data efficiency and generalization ability. It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method. Furthermore, experiments are conducted by transferring the learned pursuit policies into real-world mobile robots. Experimental results demonstrate the feasibility and potential of the proposed algorithm in learning multiple cooperative pursuit strategies.
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