公开(公告)号：US20210383225A1

公开(公告)日：2021-12-09

申请号：US17338777

申请日：2021-06-04

Applicant: DeepMind Technologies Limited

Inventor： Jean-Bastien François Laurent Grill ,  Florian Strub ,  Florent Altché ,  Corentin Tallec ,  Pierre Richemond ,  Bernardo Avila Pires ,  Zhaohan Guo ,  Mohammad Gheshlaghi Azar ,  Bilal Piot ,  Remi Munos ,  Michal Valko

IPC: G06N3/08 ,  H04L29/08

Abstract: A computer-implemented method of training a neural network. The method comprises processing a first transformed view of a training data item, e.g. an image, with a target neural network to generate a target output, processing a second transformed view of the training data item, e.g. image, with an online neural network to generate a prediction of the target output, updating parameters of the online neural network to minimize an error between the prediction of the target output and the target output, and updating parameters of the target neural network based on the parameters of the online neural network. The method can effectively train an encoder neural network without using labelled training data items, and without using a contrastive loss, i.e. without needing “negative examples” which comprise transformed views of different data items.

公开(公告)号：US20240028866A1

公开(公告)日：2024-01-25

申请号：US18334112

申请日：2023-06-13

Applicant: DeepMind Technologies Limited

Inventor： Adrià Puigdomènech Badia ,  Pablo Sprechmann ,  Alex Vitvitskyi ,  Zhaohan Guo ,  Bilal Piot ,  Steven James Kapturowski ,  Olivier Tieleman ,  Charles Blundell

IPC: G06N3/006 ,  G06N3/04 ,  G06N3/084 ,  G06F18/22 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06N3/006 ,  G06N3/04 ,  G06N3/084 ,  G06F18/22 ,  G06V10/764 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network that is used to select actions to be performed by an agent interacting with an environment. In one aspect, the method comprises: receiving an observation characterizing a current state of the environment; processing the observation and an exploration importance factor using the action selection neural network to generate an action selection output; selecting an action to be performed by the agent using the action selection output; determining an exploration reward; determining an overall reward based on: (i) the exploration importance factor, and (ii) the exploration reward; and training the action selection neural network using a reinforcement learning technique based on the overall reward.

公开(公告)号：US11714990B2

公开(公告)日：2023-08-01

申请号：US16881180

申请日：2020-05-22

Applicant: DeepMind Technologies Limited

Inventor： Adrià Puigdomènech Badia ,  Pablo Sprechmann ,  Alex Vitvitskyi ,  Zhaohan Guo ,  Bilal Piot ,  Steven James Kapturowski ,  Olivier Tieleman ,  Charles Blundell

IPC: G06N3/08 ,  G06N3/006 ,  G06N3/04 ,  G06N3/084 ,  G06F18/22 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06N3/006 ,  G06F18/22 ,  G06N3/04 ,  G06N3/084 ,  G06V10/764 ,  G06V10/82

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network that is used to select actions to be performed by an agent interacting with an environment. In one aspect, the method comprises: receiving an observation characterizing a current state of the environment; processing the observation and an exploration importance factor using the action selection neural network to generate an action selection output; selecting an action to be performed by the agent using the action selection output; determining an exploration reward; determining an overall reward based on: (i) the exploration importance factor, and (ii) the exploration reward; and training the action selection neural network using a reinforcement learning technique based on the overall reward.

公开(公告)号：US20230083486A1

公开(公告)日：2023-03-16

申请号：US17797886

申请日：2021-02-08

Applicant: DeepMind Technologies Limited

Inventor： Zhaohan Guo ,  Mohammad Gheshlaghi Azar ,  Bernardo Avila Pires ,  Florent Altché ,  Jean-Bastien François Laurent Grill ,  Bilal Piot ,  Remi Munos

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an environment representation neural network of a reinforcement learning system controls an agent to perform a given task. In one aspect, the method includes: receiving a current observation input and a future observation input; generating, from the future observation input, a future latent representation of the future state of the environment; processing, using the environment representation neural network, to generate a current internal representation of the current state of the environment; generating, from the current internal representation, a predicted future latent representation; evaluating an objective function measuring a difference between the future latent representation and the predicted future latent representation; and determining, based on a determined gradient of the objective function, an update to the current values of the environment representation parameters.

公开(公告)号：US20230059004A1

公开(公告)日：2023-02-23

申请号：US17797878

申请日：2021-02-08

Applicant: DeepMind Technologies Limited

Inventor： Adrià Puigdomènech Badia ,  Bilal Piot ,  Pablo Sprechmann ,  Steven James Kapturowski ,  Alex Vitvitskyi ,  Zhaohan Guo ,  Charles Blundell

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning with adaptive return computation schemes. In one aspect, a method includes: maintaining data specifying a policy for selecting between multiple different return computation schemes, each return computation scheme assigning a different importance to exploring the environment while performing an episode of a task; selecting, using the policy, a return computation scheme from the multiple different return computation schemes; controlling an agent to perform the episode of the task to maximize a return computed according to the selected return computation scheme; identifying rewards that were generated as a result of the agent performing the episode of the task; and updating, using the identified rewards, the policy for selecting between multiple different return computation schemes.

公开(公告)号：US20200372366A1

公开(公告)日：2020-11-26

申请号：US16881180

申请日：2020-05-22

Applicant: DeepMind Technologies Limited

Inventor： Adrià Puigdomènech Badia ,  Pablo Sprechmann ,  Alex Vitvitskyi ,  Zhaohan Guo ,  Bilal Piot ,  Steven James Kapturowski ,  Olivier Tieleman ,  Charles Blundell

IPC: G06N3/08 ,  G06K9/62 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network that is used to select actions to be performed by an agent interacting with an environment. In one aspect, the method comprises: receiving an observation characterizing a current state of the environment; processing the observation and an exploration importance factor using the action selection neural network to generate an action selection output; selecting an action to be performed by the agent using the action selection output; determining an exploration reward; determining an overall reward based on: (i) the exploration importance factor, and (ii) the exploration reward; and training the action selection neural network using a reinforcement learning technique based on the overall reward.