The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 06, 2023

Filed:

Sep. 26, 2019
Applicant:

Adobe Inc., San Jose, CA (US);

Inventors:

Georgios Theocharous, San Jose, CA (US);

Zheng Wen, Fremont, CA (US);

Yasin Abbasi Yadkori, Hanoi, VN;

Qingyun Wu, Charlottsville, VA (US);

Assignee:

Adobe Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 21/00 (2013.01); G06F 18/21 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 18/2193 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01);
Abstract

Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing offline models to warm start online bandit learner models. For example, the disclosed system can determine relevant offline models for an environment based on reward estimate differences between the offline models and the online model. The disclosed system can then utilize the relevant offline models (if any) to select an arm for the environment. The disclosed system can update the online model based on observed rewards for the selected arm. Additionally, the disclosed system can also use entropy reduction of arms to determine the utility of the arms in differentiating relevant and irrelevant offline models. For example, the disclosed system can select an arm based on a combination of the entropy reduction of the arm and the reward estimate for the arm and use the observed reward to update an observation history.


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