Yu U Chen

US Patent:

20230095706, Mar 30, 2023

Filed:

Nov 7, 2022

Appl. No.:

18/053363

Inventors:

- Atlanta GA, 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

International Classification:

G06N 3/08
G06N 3/04
H04W 24/08

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.

US Patent:

20210241090, Aug 5, 2021

Filed:

Jan 31, 2020

Appl. No.:

16/778031

Inventors:

- Atlanta GA, 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

International Classification:

G06N 3/08
G06N 3/04
H04W 24/08

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.