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Abstract—In many scenarios for informative path planning done by ground robots or drones, certain types of information are significantly more valuable than others. For example, in the precision agriculture context, detecting plant disease outbreaks can prevent costly crop losses. Quite often, there is a limit on the exploration budget, which does not allow for a detailed investigation of every location. In this paper, we propose Learned Adaptive Inspection Paths (LAIP), a methodology to learn policies that handle such scenarios by combining uniform sampling with close inspection of areas where high-value information is likely to be found. LAIP combines Q-learning in an offline reinforcement learning setting, careful engineering of the state representation and reward system, and a training regime inspired by the teacher-student curriculum learning model. We found that a policy learned with LAIP outperforms traditional approaches in low-budget scenarios.