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Isotonic Bellman calibration can dramatically reduce occupancy-balance violations in offline reinforcement learning, ensuring more reliable policy evaluations.
Discounted occupancy-ratio realizability alone can enable robust offline policy evaluation, eliminating the need for stringent completeness assumptions.
Unlock sharper regret bounds for ERM by following this guide's modular approach, which distills rate derivations into a three-step recipe and provides tools for handling nuisance components.
