TY - GEN
T1 - Gas Source Localization using Improved Multi-Agent Reinforcement Learning
AU - Wang, Zhi Pu
AU - Wu, Huai Ning
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/6
Y1 - 2020/11/6
N2 - In this paper, an improved multi-agent reinforcement learning (MARL) algorithm is proposed to solve the localization problem of gas source with disturbance sources. Firstly, based on Gaussian dispersion model, multi-point sources are estimated at the initial position of sensor network. Secondly, the multi-agent system is pre-trained in the synthetic environment derived from dispersion model and the estimated source term. Then, the improved MARL algorithm is used to guide the mobile sensors to localize the actual target source. Finally, numerical simulations are given to verify the efficiency of this method.
AB - In this paper, an improved multi-agent reinforcement learning (MARL) algorithm is proposed to solve the localization problem of gas source with disturbance sources. Firstly, based on Gaussian dispersion model, multi-point sources are estimated at the initial position of sensor network. Secondly, the multi-agent system is pre-trained in the synthetic environment derived from dispersion model and the estimated source term. Then, the improved MARL algorithm is used to guide the mobile sensors to localize the actual target source. Finally, numerical simulations are given to verify the efficiency of this method.
KW - Gas source localization (GSL)
KW - multi-agent reinforcement learning (MARL)
KW - multi-point sources
KW - source term estimation (STE)
UR - https://www.scopus.com/pages/publications/85100923056
U2 - 10.1109/CAC51589.2020.9327850
DO - 10.1109/CAC51589.2020.9327850
M3 - 会议稿件
AN - SCOPUS:85100923056
T3 - Proceedings - 2020 Chinese Automation Congress, CAC 2020
SP - 6696
EP - 6701
BT - Proceedings - 2020 Chinese Automation Congress, CAC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Chinese Automation Congress, CAC 2020
Y2 - 6 November 2020 through 8 November 2020
ER -