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Fitted Q Evaluation Without Bellman Completeness via Stationary Weighting
van der Laan, Lars, Kallus, Nathan
Fitted Q-evaluation (FQE) is a central method for off-policy evaluation in reinforcement learning, but it generally requires Bellman completeness: that the hypothesis class is closed under the evaluation Bellman operator. This requirement is challenging because enlarging the hypothesis class can worsen completeness. We show that the need for this assumption stems from a fundamental norm mismatch: the Bellman operator is gamma-contractive under the stationary distribution of the target policy, whereas FQE minimizes Bellman error under the behavior distribution. We propose a simple fix: reweight each regression step using an estimate of the stationary density ratio, thereby aligning FQE with the norm in which the Bellman operator contracts. This enables strong evaluation guarantees in the absence of realizability or Bellman completeness, avoiding the geometric error blow-up of standard FQE in this setting while maintaining the practicality of regression-based evaluation.
Zohran Mamdani drops 'insane' list of items banned at NYC Mayoral Inauguration
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How to watch Samsung's "First Look" CES 2026 presentation
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How to watch the LG press conference at CES 2026
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Could 2026 be the year we start using quantum computers for chemistry?
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