AWhat changed
Trained a probabilistic dynamics ensemble, branched short synthetic rollouts from real replay-buffer states and used model samples for soft actor-critic updates.
Scientific scope
Trained a probabilistic dynamics ensemble, branched short synthetic rollouts from real replay-buffer states and used model samples for soft actor-critic updates. Worst-case bounds alone favor zero model rollout; justification for nonzero rollouts incorporates empirical model-generalization estimates. Bounded rewards, model error and policy shift control the theoretical statements. Practical finite updates are not an exact optimizer or unconditional monotonic-improvement theorem.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Worst-case bounds alone favor zero model rollout; justification for nonzero rollouts incorporates empirical model-generalization estimates. Bounded rewards, model error and policy shift control the theoretical statements. Practical finite updates are not an exact optimizer or unconditional monotonic-improvement theorem.
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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