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:
Sep. 01, 2020

Filed:

Sep. 11, 2018
Applicant:

Sas Institute Inc., Cary, NC (US);

Inventors:

Mohammad Reza Nazari, Bethlehem, PA (US);

Afshin Oroojlooy Jadid, Bethlehem, PA (US);

Mustafa Kabul, Apex, NC (US);

Assignee:

SAS INSTITUTE INC., Cary, NC (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); G06N 3/04 (2006.01); G06F 17/18 (2006.01); G06K 9/62 (2006.01); G06N 20/00 (2019.01); G06F 16/2455 (2019.01); G06K 9/00 (2006.01); G06N 3/00 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 16/24568 (2019.01); G06F 17/18 (2013.01); G06K 9/00496 (2013.01); G06K 9/627 (2013.01); G06K 9/6256 (2013.01); G06N 3/006 (2013.01); G06N 3/04 (2013.01); G06N 3/049 (2013.01); G06N 3/0472 (2013.01); G06N 20/00 (2019.01); G06K 2209/19 (2013.01);
Abstract

Exemplary embodiments can maximize long-term value in a machine learning system. The system may employ an offline training process and an online training process. In the offline training process, an initial policy is learned to provide a warm start to the online training process. In the online training process, the system applies concurrent reinforcement learning across multiple environments, with the goal of learning efficient policies in real time from in-flight user data in one environment, and applying the learned policies to other environments. With the combination of offline training and online training, the system is able to improve initial performance through the warm start, while adapting to a changing context through concurrent reinforcement learning.


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