公开(公告)号：US20200167633A1

公开(公告)日：2020-05-28

申请号：US16615061

申请日：2018-05-22

Applicant: DEEPMIND TECHNOLOGIES LIMITED

Inventor： Misha Man Ray Denil ,  Sergio Gomez Colmenarejo ,  Serkan Cabi ,  David William Saxton ,  Joao Ferdinando Gomes de Freitas

IPC: G06N3/04 ,  G06N3/08 ,  G06K9/62

Abstract: A reinforcement learning system is proposed comprising a plurality of property detector neural networks. Each property detector neural network is arranged to receive data representing an object within an environment, and to generate property data associated with a property of the object. A processor is arranged to receive an instruction indicating a task associated with an object having an associated property, and process the output of the plurality of property detector neural networks based upon the instruction to generate a relevance data item. The relevance data item indicates objects within the environment associated with the task. The processor also generates a plurality of weights based upon the relevance data item, and, based on the weights, generates modified data representing the plurality of objects within the environment. A neural network is arranged to receive the modified data and to output an action associated with the task.

公开(公告)号：US20240394504A1

公开(公告)日：2024-11-28

申请号：US18637279

申请日：2024-04-16

Applicant: DeepMind Technologies Limited

Inventor： Misha Man Ray Denil ,  Sergio Gomez Colmenarejo ,  Serkan Cabi ,  David William Saxton ,  Joao Ferdinando Gomes de Freitas

IPC: G06N3/006 ,  G06F18/21 ,  G06F18/2451 ,  G06N3/045 ,  G06N3/047 ,  G06N3/084

Abstract: A reinforcement learning system is proposed comprising a plurality of property detector neural networks. Each property detector neural network is arranged to receive data representing an object within an environment, and to generate property data associated with a property of the object. A processor is arranged to receive an instruction indicating a task associated with an object having an associated property, and process the output of the plurality of property detector neural networks based upon the instruction to generate a relevance data item. The relevance data item indicates objects within the environment associated with the task. The processor also generates a plurality of weights based upon the relevance data item, and, based on the weights, generates modified data representing the plurality of objects within the environment. A neural network is arranged to receive the modified data and to output an action associated with the task.

公开(公告)号：US20210383222A1

公开(公告)日：2021-12-09

申请号：US17337820

申请日：2021-06-03

Applicant: DeepMind Technologies Limited

Inventor： David William Saxton ,  Eshaan Nichani

IPC: G06N3/08 ,  G06F17/18

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network by estimating the objective function curvature based on current and previous gradients. In one aspect, a method comprises: sampling a batch of training data; and for each neural network parameter: determining, based on the current batch of training data, a respective current gradient of the objective function at the current iteration with respect to the current neural network parameter; estimating an objective function curvature with respect to the current neural network parameter based on (i) the current gradient of the objective function at the current iteration, and (ii) a respective previous gradient of the objective function at each of a plurality of previous iterations; and updating a current value of the neural network parameter based on the estimate of the curvature of the objective function.