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Presents an uncertainty-aware geosteering framework integrating particle filtering for probabilistic subsurface interpretation with reinforcement learning for sequential decision-making, evaluated on an industrial simulator.
This paper studies when and how a planner should supplement a pre-trained simulator with real experiments in sequential decision problems, proposing Fisher-SEP to minimize posterior variance of a target policy's value.