AWhat changed
Conditioned a neural value model on sampled quantile fractions to approximate return quantile functions, supporting distributional learning and distorted-quantile risk-sensitive action evaluation.
Scientific scope
Conditioned a neural value model on sampled quantile fractions to approximate return quantile functions, supporting distributional learning and distorted-quantile risk-sensitive action evaluation. Extends QR-DQN rather than inventing distributional RL or quantile regression. Finite-capacity stochastic quantile sampling is not exact representation of arbitrary return laws; risk-distortion experiments do not establish universally optimal risk-sensitive control or neural convergence.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Extends QR-DQN rather than inventing distributional RL or quantile regression. Finite-capacity stochastic quantile sampling is not exact representation of arbitrary return laws; risk-distortion experiments do not establish universally optimal risk-sensitive control or neural convergence.
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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