What is recorded here?
Demonstrated parallel natural-evolution-strategy style optimization of deep policy parameters using shared random perturbations and scalar fitness communication.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Evolution strategies, NES and score-function gradient estimators predate this work; credited contribution is the scalable deep-policy implementation and empirical study. Wall-clock results use hundreds/thousands of CPUs and often more environment samples than comparators.
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People and systems
Tim Salimans; Jonathan Ho; Xi Chen; Ilya Sutskever
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.
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English catalogue narrative
Scalable evolution-strategy training of deep control policies
Demonstrated parallel natural-evolution-strategy style optimization of deep policy parameters using shared random perturbations and scalar fitness communication.
Scientific scope
Evolution strategies, NES and score-function gradient estimators predate this work; credited contribution is the scalable deep-policy implementation and empirical study. Wall-clock results use hundreds/thousands of CPUs and often more environment samples than comparators.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Tim Salimans; Jonathan Ho; Xi Chen; Ilya Sutskever
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.