AWhat changed
Adapted Double Q-learning to deep Q-networks by selecting next actions with the online network and evaluating them with a separate target network, reducing maximization overestimation in tested tasks.
Scientific scope
Adapted Double Q-learning to deep Q-networks by selecting next actions with the online network and evaluating them with a separate target network, reducing maximization overestimation in tested tasks. Extends earlier tabular Double Q-learning rather than inventing it. Reducing observed overestimation does not make every learned critic unbiased, remove possible underestimation or guarantee neural convergence; Atari results concern specified protocols.
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 tabular Double Q-learning rather than inventing it. Reducing observed overestimation does not make every learned critic unbiased, remove possible underestimation or guarantee neural convergence; Atari results concern specified protocols.
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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