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:
Nov. 08, 2022

Filed:

Jan. 31, 2020
Applicant:

At&t Intellectual Property I, L.p., Atlanta, GA (US);

Inventors:

Jie Chen, Watchung, NJ (US);

Wenjie Zhao, Princeton, NJ (US);

Ganesh Krishnamurthi, Danville, CA (US);

Huahui Wang, Bridgewater, NJ (US);

Huijing Yang, Princeton, NJ (US);

Yu Chen, Pittsburgh, PA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 13/02 (2006.01); G06E 1/00 (2006.01); G06G 7/00 (2006.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01); H04W 24/08 (2009.01); H04W 84/04 (2009.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06N 3/0445 (2013.01); G06N 3/0454 (2013.01); H04W 24/08 (2013.01); H04W 84/042 (2013.01);
Abstract

A processing system including at least one processor may obtain operational data from a radio access network (RAN), format the operational data into state information and reward information for a reinforcement learning agent (RLA), processing the state information and the reward information via the RLA, where the RLA comprises a plurality of sub-agents, each comprising a respective neural network, each of the neural networks encoding a respective policy for selecting at least one setting of at least one parameter of the RAN to increase a respective predicted reward in accordance with the state information, and where each neural network is updated in accordance with the reward information. The processing system may further determine settings for parameters of the RAN via the RLA, where the RLA determines the settings in accordance with selections for the settings via the plurality of sub-agents, and apply the plurality of settings to the RAN.


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