@inproceedings{19bfe19261804a4a8070761dd335a9a2,
title = "Releasing source locating based on Multi-Agent Reinforcement Learning with reward function designed by maximum entropy",
abstract = "This paper is focused on locating the actual releasing source in the environment of multiple disturbance sources. The actual releasing source is located with multiple mobile sensors. In an attempt to avoid mobile sensors falling into the disturbance releasing source and gather at the actual releasing source quickly, an improved Multi-Agent Reinforcement Learning (MARL) with novel designed reward function is applied to guide the movement of mobile sensors. To ensure finding the actual releasing source with maximum releasing concentration, the reward function is designed based on maximum entropy (ME). Finally, MARL with reward function designed by ME and normal MARL are simulated and compared to verify the efficiency and advantage of this method.",
keywords = "Maximum entropy, Multi-Agent Reinforcement Learning, Releasing source locating",
author = "Wang, {Zhi Pu} and Zeng, {Guang Rong} and Deng, {Lie Wei} and Wang Cao and Yao Guo",
note = "Publisher Copyright: {\textcopyright} 2022 Technical Committee on Control Theory, Chinese Association of Automation.; 41st Chinese Control Conference, CCC 2022 ; Conference date: 25-07-2022 Through 27-07-2022",
year = "2022",
doi = "10.23919/CCC55666.2022.9902336",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "4688--4693",
editor = "Zhijun Li and Jian Sun",
booktitle = "Proceedings of the 41st Chinese Control Conference, CCC 2022",
address = "United States",
}