公开(公告)号：US20200244707A1

公开(公告)日：2020-07-30

申请号：US16752496

申请日：2020-01-24

Applicant: DeepMind Technologies Limited

Inventor： David Silver ,  Oriol Vinyals ,  Maxwell Elliot Jaderberg

IPC: H04L29/06 ,  G06N3/08 ,  G06K9/62

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network having a plurality of policy parameters and used to select actions to be performed by an agent to control the agent to perform a particular task while interacting with one or more other agents in an environment. In one aspect, the method includes: maintaining data specifying a pool of candidate action selection policies; maintaining data specifying respective matchmaking policy; and training the policy neural network using a reinforcement learning technique to update the policy parameters. The policy parameters define policies to be used in controlling the agent to perform the particular task.

公开(公告)号：US20200184316A1

公开(公告)日：2020-06-11

申请号：US16620815

申请日：2018-06-11

Applicant: DEEPMIND TECHNOLOGIES LIMITED

Inventor： Koray Kavukcuoglu ,  Aaron Gerard Antonius van den Oord ,  Oriol Vinyals

IPC: G06N3/04 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating discrete latent representations of input data items. One of the methods includes receiving an input data item; providing the input data item as input to an encoder neural network to obtain an encoder output for the input data item; and generating a discrete latent representation of the input data item from the encoder output, comprising: for each of the latent variables, determining, from a set of latent embedding vectors in the memory, a latent embedding vector that is nearest to the encoded vector for the latent variable.

公开(公告)号：US20200090006A1

公开(公告)日：2020-03-19

申请号：US16689058

申请日：2019-11-19

Applicant: DeepMind Technologies Limited

Inventor： Daniel Pieter Wierstra ,  Yujia Li ,  Razvan Pascanu ,  Peter William Battaglia ,  Theophane Guillaume Weber ,  Lars Buesing ,  David Paul Reichert ,  Arthur Clement Guez ,  Danilo Jimenez Rezende ,  Adrià Puigdomènech Badia ,  Oriol Vinyals ,  Nicolas Manfred Otto Heess ,  Sebastien Henri Andre Racaniere

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06K9/68

Abstract: A neural network system is proposed. The neural network can be trained by model-based reinforcement learning to select actions to be performed by an agent interacting with an environment, to perform a task in an attempt to achieve a specified result. The system may comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.

公开(公告)号：US20180365554A1

公开(公告)日：2018-12-20

申请号：US15985463

申请日：2018-05-21

Applicant: DeepMind Technologies Limited

Inventor： Aaron Gerard Antonius van den Oord ,  Karen Simonyan ,  Oriol Vinyals

IPC: G06N3/04 ,  G06N3/08

Abstract: A feedforward generative neural network that generates an output example that includes multiple output samples of a particular type in a single neural network inference. Optionally, the generation may be conditioned on a context input. For example, the feedforward generative neural network may generate a speech waveform that is a verbalization of an input text segment conditioned on linguistic features of the text segment.

公开(公告)号：US20180322891A1

公开(公告)日：2018-11-08

申请号：US16030742

申请日：2018-07-09

Applicant: DeepMind Technologies Limited

Inventor： Aaron Gerard Antonius van den Oord ,  Sander Etienne Lea Dieleman ,  Nal Emmerich Kalchbrenner ,  Karen Simonyan ,  Oriol Vinyals

IPC: G10L25/30 ,  G06N3/04

CPC classification number: G10L25/30 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/0481 ,  G10H2250/311 ,  G10L13/06

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output sequence of audio data that comprises a respective audio sample at each of a plurality of time steps. One of the methods includes, for each of the time steps: providing a current sequence of audio data as input to a convolutional subnetwork, wherein the current sequence comprises the respective audio sample at each time step that precedes the time step in the output sequence, and wherein the convolutional subnetwork is configured to process the current sequence of audio data to generate an alternative representation for the time step; and providing the alternative representation for the time step as input to an output layer, wherein the output layer is configured to: process the alternative representation to generate an output that defines a score distribution over a plurality of possible audio samples for the time step.