AWhat changed
Approximated batch-supported actions with a conditional variational autoencoder, sampled candidates and bounded learned perturbations, then selected actions using a learned critic.
Scientific scope
Approximated batch-supported actions with a conditional variational autoencoder, sampled candidates and bounded learned perturbations, then selected actions using a learned critic. Original v1 deep method is an approximation to the discrete batch-constrained analysis, not a certified support oracle. Discrete results require dataset closure and support assumptions; global optimality additionally requires support for an optimal policy. Later implementation variants are not silently imported.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Original v1 deep method is an approximation to the discrete batch-constrained analysis, not a certified support oracle. Discrete results require dataset closure and support assumptions; global optimality additionally requires support for an optimal policy. Later implementation variants are not silently imported.
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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