AWhat changed
Augmented Bellman regression with a conservative Q-value penalty, contrasting policy/proposal actions with data actions to reduce value overestimation during offline learning.
Scientific scope
Augmented Bellman regression with a conservative Q-value penalty, contrasting policy/proposal actions with data actions to reduce value overestimation during offline learning. The tighter objective bounds policy-expected value under stated support, regularization and policy-change assumptions; it is not a pointwise lower bound for every action. Main tabular statements and practical nonlinear optimization are distinct; empirical results do not certify universal safe improvement.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. The tighter objective bounds policy-expected value under stated support, regularization and policy-change assumptions; it is not a pointwise lower bound for every action. Main tabular statements and practical nonlinear optimization are distinct; empirical results do not certify universal safe improvement.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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