In this talk I present my research on using techniques from reinforcement learning for efficiently sampling scientific data in large-scale outdoor environments. The techniques presented generate paths to efficiently measure and then mathematically model a scalar field by performing non-uniform measurements in a given region of interest. In particular, the class of scalar fields considered are some physical or virtual parameters that vary spatially, such as depth of the sea floor or algae blooms or suspended particles in air. As the measurements are collected at each sampling location, they are used to compute an estimate of the large-scale variation of the phenomenon of interest. I present techniques to compute a sampling path that minimizes the expected time to accurately model the phenomenon of interest by visiting high information regions (hotspots) using non-myopic path generation based on reinforcement learning. I will briefly talk about the platforms built and used for evaluating these sampling algorithms in real world applications and also mention the challenges involved in conducting field experiments in harsh outdoor environments.