公开(公告)号：US20230095706A1

公开(公告)日：2023-03-30

申请号：US18053363

申请日：2022-11-07

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Jie Chen ,  Wenjie Zhao ,  Ganesh Krishnamurthi ,  Huahui Wang ,  Huijing Yang ,  Yu Chen

IPC: G06N3/08 ,  G06N3/04 ,  H04W24/08

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.

公开(公告)号：US20210241090A1

公开(公告)日：2021-08-05

申请号：US16778031

申请日：2020-01-31

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Jie Chen ,  Wenjie Zhao ,  Ganesh Krishnamurthi ,  Huahui Wang ,  Huijing Yang ,  Yu Chen

IPC: G06N3/08 ,  G06N3/04 ,  H04W24/08

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.

公开(公告)号：US11494649B2

公开(公告)日：2022-11-08

申请号：US16778031

申请日：2020-01-31

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Jie Chen ,  Wenjie Zhao ,  Ganesh Krishnamurthi ,  Huahui Wang ,  Huijing Yang ,  Yu Chen

IPC: G05B13/02 ,  G06E1/00 ,  G06G7/00 ,  G06N3/08 ,  G06N3/04 ,  H04W24/08 ,  H04W84/04

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.