AWhat changed
Decouples trajectory-generating actors from batched learners and uses truncated-importance V-trace targets to compensate actor-policy lag in high-throughput multi-task RL.
Scientific scope
Decouples trajectory-generating actors from batched learners and uses truncated-importance V-trace targets to compensate actor-policy lag in high-throughput multi-task RL. Finite rho truncation evaluates a policy between behavior and target rather than exactly the current target; c truncation controls traces differently. Tabular/operator guarantees are not a convergence proof for arbitrary deep function approximation. First three authors are equal contributors; printed Volodymir Mnih is the source label, not an independently resolved identity correction.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Finite rho truncation evaluates a policy between behavior and target rather than exactly the current target; c truncation controls traces differently. Tabular/operator guarantees are not a convergence proof for arbitrary deep function approximation. First three authors are equal contributors; printed Volodymir Mnih is the source label, not an independently resolved identity correction.
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.
Full scientific record · Browse contribution grades · Request a correction