  The accurate estimation of locations and emission rates of gas sources is
crucial across various domains, including environmental monitoring and
greenhouse gas emission analysis. This study investigates two drone sampling
strategies for inferring source term parameters of gas plumes from atmospheric
measurements. Both strategies are guided by the goal of maximizing information
gain attained from observations at sequential locations. Our research compares
the myopic approach of infotaxis to a far-sighted navigation strategy trained
through deep reinforcement learning. We demonstrate the superior performance of
deep reinforcement learning over infotaxis in environments with non-isotropic
gas plumes.
