Gas Source Localization using Improved Multi-Agent Reinforcement Learning

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.