AWhat changed
Combined deterministic actor-critic policy gradients with replay, slowly updated targets and normalization to train deep policies over continuous actions.
Scientific scope
Combined deterministic actor-critic policy gradients with replay, slowly updated targets and normalization to train deep policies over continuous actions. Extends earlier deterministic policy-gradient algorithms and DQN stabilization, not invention of either. More than20simulated physics tasks and selected pixel-control results do not ensure convergence, physical deployment safety or universal control performance; first two authors source-marked equal contribution.
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 earlier deterministic policy-gradient algorithms and DQN stabilization, not invention of either. More than20simulated physics tasks and selected pixel-control results do not ensure convergence, physical deployment safety or universal control performance; first two authors source-marked equal contribution.
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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