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.

公开(公告)号：US11741334B2

公开(公告)日：2023-08-29

申请号：US16882373

申请日：2020-05-22

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: G06N3/08 ,  G06N3/006 ,  G06N3/088 ,  G06F18/21 ,  G06F18/214 ,  G06N3/045

CPC classification number: G06N3/006 ,  G06F18/2148 ,  G06F18/2185 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088

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.

公开(公告)号：US20240062035A1

公开(公告)日：2024-02-22

申请号：US18351440

申请日：2023-07-12

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: G06N3/006 ,  G06N3/08 ,  G06N3/088 ,  G06F18/21 ,  G06F18/214 ,  G06N3/045

CPC classification number: G06N3/006 ,  G06N3/08 ,  G06N3/088 ,  G06F18/2185 ,  G06F18/2148 ,  G06N3/045

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.

公开(公告)号：US20200285909A1

公开(公告)日：2020-09-10

申请号：US16882373

申请日：2020-05-22

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 ,  G06N3/04 ,  G06N3/08

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.

公开(公告)号：US10664725B2

公开(公告)日：2020-05-26

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: G06N3/08 ,  G06K9/62 ,  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.