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Soft Fitted Q-Iteration without Bellman Completeness: Occupancy Reweighting and Temperature Annealing
Fitted \(Q\)-iteration (FQI) is a standard regression-based method for optimal control in offline reinforcement learning, but its stability under function approximation often relies on Bellman completeness, which requires Bellman images of the fitted class to remain in the class. We study Kullback--
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