公开(公告)号：US11334792B2

公开(公告)日：2022-05-17

申请号：US16403388

申请日：2019-05-03

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Adria Puigdomenech Badia ,  Alexander Benjamin Graves ,  Timothy James Alexander Harley ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.

公开(公告)号：US11868894B2

公开(公告)日：2024-01-09

申请号：US18149771

申请日：2023-01-04

Applicant: DeepMind Technologies Limited

Inventor： Hubert Josef Soyer ,  Lasse Espeholt ,  Karen Simonyan ,  Yotam Doron ,  Vlad Firoiu ,  Volodymyr Mnih ,  Koray Kavukcuoglu ,  Remi Munos ,  Thomas Ward ,  Timothy James Alexander Harley ,  Iain Robert Dunning

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.

公开(公告)号：US20210097443A1

公开(公告)日：2021-04-01

申请号：US16586236

申请日：2019-09-27

Applicant: DeepMind Technologies Limited

Inventor： Ang Li ,  Valentin Clement Dalibard ,  David Budden ,  Ola Spyra ,  Maxwell Elliot Jaderberg ,  Timothy James Alexander Harley ,  Sagi Perel ,  Chenjie Gu ,  Pramod Gupta

IPC: G06N20/20 ,  G06N5/04 ,  G06F16/901

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. A method includes: maintaining a plurality of training sessions; assigning, to each worker of one or more workers, a respective training session of the plurality of training sessions; repeatedly performing operations until meeting one or more termination criteria, the operations comprising: receiving an updated training session from a respective worker of the one or more workers, selecting a second training session, selecting, based on comparing the updated training session and the second training session using a fitness evaluation function, either the updated training session or the second training session as a parent training session, generating a child training session from the selected parent training session, and assigning the child training session to an available worker, and selecting a candidate model to be a trained model for the machine learning model.

公开(公告)号：US10936946B2

公开(公告)日：2021-03-02

申请号：US15349950

申请日：2016-11-11

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Adrià Puigdomènech Badia ,  Alexander Benjamin Graves ,  Timothy James Alexander Harley ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.

公开(公告)号：US20190258929A1

公开(公告)日：2019-08-22

申请号：US16403388

申请日：2019-05-03

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Adria Puigdomenech Badia ,  Alexander Benjamin Graves ,  Timothy James Alexander Harley ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.

公开(公告)号：US12299574B2

公开(公告)日：2025-05-13

申请号：US18487428

申请日：2023-10-16

Applicant: DeepMind Technologies Limited

Inventor： Hubert Josef Soyer ,  Lasse Espeholt ,  Karen Simonyan ,  Yotam Doron ,  Vlad Firoiu ,  Volodymyr Mnih ,  Koray Kavukcuoglu ,  Remi Munos ,  Thomas Ward ,  Timothy James Alexander Harley ,  Iain Robert Dunning

IPC: G06N3/08 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.

公开(公告)号：US20240362481A1

公开(公告)日：2024-10-31

申请号：US18662481

申请日：2024-05-13

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Adrià Puigdomènech Badia ,  Alexander Benjamin Graves ,  Timothy James Alexander Harley ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/04 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.

公开(公告)号：US20240127060A1

公开(公告)日：2024-04-18

申请号：US18487428

申请日：2023-10-16

Applicant: DeepMind Technologies Limited

Inventor： Hubert Josef Soyer ,  Lasse Espeholt ,  Karen Simonyan ,  Yotam Doron ,  Vlad Firoiu ,  Volodymyr Mnih ,  Koray Kavukcuoglu ,  Remi Munos ,  Thomas Ward ,  Timothy James Alexander Harley ,  Iain Robert Dunning

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.

公开(公告)号：US20230153617A1

公开(公告)日：2023-05-18

申请号：US18149771

申请日：2023-01-04

Applicant: DeepMind Technologies Limited

Inventor： Hubert Josef Soyer ,  Lasse Espeholt ,  Karen Simonyan ,  Yotam Doron ,  Vlad Firoiu ,  Volodymyr Mnih ,  Koray Kavukcuoglu ,  Remi Munos ,  Thomas Ward ,  Timothy James Alexander Harley ,  Iain Robert Dunning

IPC: G06N3/08 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.

公开(公告)号：US20210166127A1

公开(公告)日：2021-06-03

申请号：US17170316

申请日：2021-02-08

Applicant: DeepMind Technologies Limited

Inventor： Volodymyr Mnih ,  Adrià Puigdomènech Badia ,  Alexander Benjamin Graves ,  Timothy James Alexander Harley ,  David Silver ,  Koray Kavukcuoglu

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.