What is recorded here?
Reduced sequential imitation learning to no-regret online learning and introduced an iterative data-aggregation algorithm that trains on states induced by the learned policy.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact three-author AISTATS 2011 paper. Its guarantees require the reduction assumptions stated in the paper; it does not establish safety, universal imitation performance or authorship of all later DAgger variants.
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People and systems
Stephane Ross; Geoffrey J. Gordon; J. Andrew Bagnell
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
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Documented countries / regions: United States
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English catalogue narrative
Ross, Gordon and Bagnell: DAgger imitation learning
Reduced sequential imitation learning to no-regret online learning and introduced an iterative data-aggregation algorithm that trains on states induced by the learned policy.
Scientific scope
The record is the exact three-author AISTATS 2011 paper. Its guarantees require the reduction assumptions stated in the paper; it does not establish safety, universal imitation performance or authorship of all later DAgger variants.
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
Stephane Ross; Geoffrey J. Gordon; J. Andrew Bagnell
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.