公开(公告)号：US11662210B2

公开(公告)日：2023-05-30

申请号：US17747144

申请日：2022-05-18

Applicant: DeepMind Technologies Limited

Inventor： Andrea Banino ,  Sudarshan Kumaran ,  Raia Thais Hadsell ,  Benigno Uria-Martínez

IPC: G01C21/20 ,  G06T7/73 ,  G06N3/04 ,  G06N3/08 ,  G06N3/06 ,  G06N3/082 ,  G06N3/006

CPC classification number: G01C21/20 ,  G06N3/04 ,  G06N3/0445 ,  G06N3/0454 ,  G06N3/08 ,  G06N3/082 ,  G06T7/73 ,  G06N3/006 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a grid cell neural network and an action selection neural network. The grid cell network is configured to: receive an input comprising data characterizing a velocity of the agent; process the input to generate a grid cell representation; and process the grid cell representation to generate an estimate of a position of the agent in the environment; the action selection neural network is configured to: receive an input comprising a grid cell representation and an observation characterizing a state of the environment; and process the input to generate an action selection network output.

公开(公告)号：US20220355472A1

公开(公告)日：2022-11-10

申请号：US17872528

申请日：2022-07-25

Applicant: DeepMind Technologies Limited

Inventor： Razvan Pascanu ,  Raia Thais Hadsell ,  Mel Vecerik ,  Thomas Rothoerl ,  Andrei-Alexandru Rusu ,  Nicolas Manfred Otto Heess

IPC: B25J9/16 ,  G06N3/04 ,  G06N3/08 ,  G05B13/02 ,  G06N3/00

Abstract: A system includes a neural network system implemented by one or more computers. The neural network system is configured to receive an observation characterizing a current state of a real-world environment being interacted with by a robotic agent to perform a robotic task and to process the observation to generate a policy output that defines an action to be performed by the robotic agent in response to the observation. The neural network system includes: (i) a sequence of deep neural networks (DNNs), in which the sequence of DNNs includes a simulation-trained DNN that has been trained on interactions of a simulated version of the robotic agent with a simulated version of the real-world environment to perform a simulated version of the robotic task, and (ii) a first robot-trained DNN that is configured to receive the observation and to process the observation to generate the policy output.

公开(公告)号：US10949734B2

公开(公告)日：2021-03-16

申请号：US15396319

申请日：2016-12-30

Applicant: DeepMind Technologies Limited

Inventor： Neil Charles Rabinowitz ,  Guillaume Desjardins ,  Andrei-Alexandru Rusu ,  Koray Kavukcuoglu ,  Raia Thais Hadsell ,  Razvan Pascanu ,  James Kirkpatrick ,  Hubert Josef Soyer

IPC: G06N3/04 ,  G06N3/08 ,  G06F17/16

Abstract: Methods and systems for performing a sequence of machine learning tasks. One system includes a sequence of deep neural networks (DNNs), including: a first DNN corresponding to a first machine learning task, wherein the first DNN comprises a first plurality of indexed layers, and each layer in the first plurality of indexed layers is configured to receive a respective layer input and process the layer input to generate a respective layer output; and one or more subsequent DNNs corresponding to one or more respective machine learning tasks, wherein each subsequent DNN comprises a respective plurality of indexed layers, and each layer in a respective plurality of indexed layers with index greater than one receives input from a preceding layer of the respective subsequent DNN, and one or more preceding layers of respective preceding DNNs, wherein a preceding layer is a layer whose index is one less than the current index.

公开(公告)号：US20200223063A1

公开(公告)日：2020-07-16

申请号：US16829237

申请日：2020-03-25

Applicant: DeepMind Technologies Limited

Inventor： Razvan Pascanu ,  Raia Thais Hadsell ,  Mel Vecerik ,  Thomas Rothoerl ,  Andrei-Alexandru Rusu ,  Nicolas Manfred Otto Heess

IPC: B25J9/16 ,  G06N3/04 ,  G06N3/08 ,  G05B13/02 ,  G06N3/00

Abstract: A system includes a neural network system implemented by one or more computers. The neural network system is configured to receive an observation characterizing a current state of a real-world environment being interacted with by a robotic agent to perform a robotic task and to process the observation to generate a policy output that defines an action to be performed by the robotic agent in response to the observation. The neural network system includes: (i) a sequence of deep neural networks (DNNs), in which the sequence of DNNs includes a simulation-trained DNN that has been trained on interactions of a simulated version of the robotic agent with a simulated version of the real-world environment to perform a simulated version of the robotic task, and (ii) a first robot-trained DNN that is configured to receive the observation and to process the observation to generate the policy output.

公开(公告)号：US20200151515A1

公开(公告)日：2020-05-14

申请号：US16745757

申请日：2020-01-17

Applicant: DeepMind Technologies Limited

Inventor： Fabio Viola ,  Piotr Wojciech Mirowski ,  Andrea Banino ,  Razvan Pascanu ,  Hubert Josef Soyer ,  Andrew James Ballard ,  Sudarshan Kumaran ,  Raia Thais Hadsell ,  Laurent Sifre ,  Rostislav Goroshin ,  Koray Kavukcuoglu ,  Misha Man Ray Denil

IPC: G06K9/62 ,  G06N3/00 ,  G06N3/08 ,  G06N3/04 ,  G06K9/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. In one aspect, a method of training an action selection policy neural network for use in selecting actions to be performed by an agent navigating through an environment to accomplish one or more goals comprises: receiving an observation image characterizing a current state of the environment; processing, using the action selection policy neural network, an input comprising the observation image to generate an action selection output; processing, using a geometry-prediction neural network, an intermediate output generated by the action selection policy neural network to predict a value of a feature of a geometry of the environment when in the current state; and backpropagating a gradient of a geometry-based auxiliary loss into the action selection policy neural network to determine a geometry-based auxiliary update for current values of the network parameters.