公开(公告)号：US11468321B2

公开(公告)日：2022-10-11

申请号：US16624245

申请日：2018-06-28

Applicant: DEEPMIND TECHNOLOGIES LIMITED

Inventor： Olivier Claude Pietquin ,  Martin Riedmiller ,  Wang Fumin ,  Bilal Piot ,  Mel Vecerik ,  Todd Andrew Hester ,  Thomas Rothoerl ,  Thomas Lampe ,  Nicolas Manfred Otto Heess ,  Jonathan Karl Scholz

IPC: G06N3/02 ,  G06N3/08 ,  G06N3/04

Abstract: An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.

公开(公告)号：US20200151562A1

公开(公告)日：2020-05-14

申请号：US16624245

申请日：2018-06-28

Applicant: DEEPMIND TECHNOLOGIES LIMITED

Inventor： Olivier Pietquin ,  Martin Riedmiller ,  Wang Fumin ,  Bilal Piot ,  Mel Vecerik ,  Todd Andrew Hester ,  Thomas Rothörl ,  Thomas Lampe ,  Nicolas Manfred Otto Heess ,  Jonathan Karl Scholz

IPC: G06N3/08 ,  G06N3/04

Abstract: An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.

公开(公告)号：US20240303897A1

公开(公告)日：2024-09-12

申请号：US18600552

申请日：2024-03-08

Applicant: DeepMind Technologies Limited

Inventor： Carl Doersch ,  Yi Yang ,  Mel Vecerik ,  Dilara Gokay ,  Ankush Gupta ,  Yusuf Aytar ,  Joao Carreira ,  Andrew Zisserman

IPC: G06T13/80 ,  G06T3/18 ,  G06T7/70 ,  G06T9/00

CPC classification number: G06T13/80 ,  G06T3/18 ,  G06T7/70 ,  G06T9/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for animating images using point trajectories.

公开(公告)号：US20240042600A1

公开(公告)日：2024-02-08

申请号：US18331632

申请日：2023-06-08

Applicant: DeepMind Technologies Limited

Inventor： Serkan Cabi ,  Ziyu Wang ,  Alexander Novikov ,  Ksenia Konyushkova ,  Sergio Gomez Colmenarejo ,  Scott Ellison Reed ,  Misha Man Ray Denil ,  Jonathan Karl Scholz ,  Oleg O. Sushkov ,  Rae Chan Jeong ,  David Barker ,  David Budden ,  Mel Vecerik ,  Yusuf Aytar ,  Joao Ferdinando Gomes de Freitas

IPC: B25J9/16

CPC classification number: B25J9/161 ,  B25J9/163 ,  B25J9/1661

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

公开(公告)号：US11886997B2

公开(公告)日：2024-01-30

申请号：US17962008

申请日：2022-10-07

Applicant: DeepMind Technologies Limited

Inventor： Olivier Pietquin ,  Martin Riedmiller ,  Wang Fumin ,  Bilal Piot ,  Mel Vecerik ,  Todd Andrew Hester ,  Thomas Rothoerl ,  Thomas Lampe ,  Nicolas Manfred Otto Heess ,  Jonathan Karl Scholz

IPC: G06N3/08 ,  G06N3/045 ,  G06N3/047

CPC classification number: G06N3/08 ,  G06N3/045 ,  G06N3/047

Abstract: An off-policy reinforcement learning actor-critic neural network system configured to select actions from a continuous action space to be performed by an agent interacting with an environment to perform a task. An observation defines environment state data and reward data. The system has an actor neural network which learns a policy function mapping the state data to action data. A critic neural network learns an action-value (Q) function. A replay buffer stores tuples of the state data, the action data, the reward data and new state data. The replay buffer also includes demonstration transition data comprising a set of the tuples from a demonstration of the task within the environment. The neural network system is configured to train the actor neural network and the critic neural network off-policy using stored tuples from the replay buffer comprising tuples both from operation of the system and from the demonstration transition data.

公开(公告)号：US11712799B2

公开(公告)日：2023-08-01

申请号：US17020294

申请日：2020-09-14

Applicant: DeepMind Technologies Limited

Inventor： Serkan Cabi ,  Ziyu Wang ,  Alexander Novikov ,  Ksenia Konyushkova ,  Sergio Gomez Colmenarejo ,  Scott Ellison Reed ,  Misha Man Ray Denil ,  Jonathan Karl Scholz ,  Oleg O. Sushkov ,  Rae Chan Jeong ,  David Barker ,  David Budden ,  Mel Vecerik ,  Yusuf Aytar ,  Joao Ferdinando Gomes de Freitas

IPC: B25J9/16

CPC classification number: B25J9/161 ,  B25J9/163 ,  B25J9/1661

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

公开(公告)号：US20210078169A1

公开(公告)日：2021-03-18

申请号：US17020294

申请日：2020-09-14

Applicant: DeepMind Technologies Limited

Inventor： Serkan Cabi ,  Ziyu Wang ,  Alexander Novikov ,  Ksenia Konyushkova ,  Sergio Gomez Colmenarejo ,  Scott Ellison Reed ,  Misha Man Ray Denil ,  Jonathan Karl Scholz ,  Oleg O. Sushkov ,  Rae Chan Jeong ,  David Barker ,  David Budden ,  Mel Vecerik ,  Yusuf Aytar ,  Joao Ferdinando Gomes de Freitas

IPC: B25J9/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

公开(公告)号：US20190354813A1

公开(公告)日：2019-11-21

申请号：US16528260

申请日：2019-07-31

Applicant: DeepMind Technologies Limited

Inventor： Martin Riedmiller ,  Roland Hafner ,  Mel Vecerik ,  Timothy Paul Lillicrap ,  Thomas Lampe ,  Ivaylo Popov ,  Gabriel Barth-Maron ,  Nicolas Manfred Otto Heess

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-efficient reinforcement learning. One of the systems is a system for training an actor neural network used to select actions to be performed by an agent that interacts with an environment by receiving observations characterizing states of the environment and, in response to each observation, performing an action selected from a continuous space of possible actions, wherein the actor neural network maps observations to next actions in accordance with values of parameters of the actor neural network, and wherein the system comprises: a plurality of workers, wherein each worker is configured to operate independently of each other worker, wherein each worker is associated with a respective agent replica that interacts with a respective replica of the environment during the training of the actor neural network.