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Q-MMR framework offers novel approach to off-policy evaluation

By PulseAugur Editorial · [4 sources] ·

Researchers have introduced Q-MMR, a new theoretical framework for off-policy evaluation in Markov Decision Processes (MDPs). This method learns weights for data points to approximate expected returns under a target policy, utilizing a moment-matching objective. A key finding is a data-dependent, dimension-free finite-sample guarantee for general function approximation, which is notable for not depending on the complexity of the function class. AI

IMPACT Introduces a novel theoretical framework for off-policy evaluation, potentially improving reinforcement learning agent training.

RANK_REASON The cluster contains an academic paper detailing a new theoretical framework for a machine learning problem.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Q-MMR framework offers novel approach to off-policy evaluation

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The cluster contains an academic paper detailing a new theoretical framework for a machine learning problem.
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COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Xiang Li, Nan Jiang ·

    Q-MMR: Off-Policy Evaluation via Recursive Reweighting and Moment Matching

    arXiv:2605.06474v1 Announce Type: new Abstract: 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…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Q-MMR: Off-Policy Evaluation via Recursive Reweighting and Moment Matching

    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 indu…

  3. arXiv stat.ML TIER_1 English(EN) · Nan Jiang ·

    Q-MMR: Off-Policy Evaluation via Recursive Reweighting and Moment Matching

    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 indu…

  4. arXiv stat.ML TIER_1 English(EN) · Nan Jiang ·

    Q-MMR: Off-Policy Evaluation via Recursive Reweighting and Moment Matching

    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 indu…

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