AI RESEARCH
Q-MMR: Off-Policy Evaluation via Recursive Reweighting and Moment Matching
arXiv CS.LG
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ArXi:2605.06474v1 Announce Type: new We present a novel theoretical framework, Q-MMR, for off-policy evaluation in finite-horizon MDPs. Q-MMR learns a set of scalar weights, one for each data point, such that the reweighted rewards approximate the expected return under the target policy. The weights are learned inductively in a top-down manner via a moment matching objective against a value-function discriminator class.