AWhat changed
Represented return laws by uniformly weighted learned quantile locations and used quantile regression updates, connecting approximation to a Wasserstein-minimizing projection.
Scientific scope
Represented return laws by uniformly weighted learned quantile locations and used quantile regression updates, connecting approximation to a Wasserstein-minimizing projection. Uses inherited quantile regression, not its invention. Source Proposition2 contracts the quantile-projected fixed-policy Bellman operator in maximal infinity-Wasserstein distance for countable state/action spaces; not arbitrary neural greedy-control convergence or contraction in every finite-p Wasserstein metric. Empirical sample-Wasserstein loss itself can have biased minimizers.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Uses inherited quantile regression, not its invention. Source Proposition2 contracts the quantile-projected fixed-policy Bellman operator in maximal infinity-Wasserstein distance for countable state/action spaces; not arbitrary neural greedy-control convergence or contraction in every finite-p Wasserstein metric. Empirical sample-Wasserstein loss itself can have biased minimizers.
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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