公开(公告)号：US10860895B2

公开(公告)日：2020-12-08

申请号：US16689017

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 ,  Oriol Vinyals ,  Nicolas Manfred Otto Heess ,  Sebastien Henri Andre Racaniere

IPC: G06K9/62 ,  G06K9/68 ,  G06N3/08 ,  G06N5/04

Abstract: A neural network system is proposed 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 include a controller to receive state data and context data, and to output action data. The system may also include an imagination module to receive the state and action data, and to output consequent state data. The system may also include a manager to receive the state data and the context data, and to output route data which defines whether the system is to execute an action or to imagine. The system may also include a memory to store the context data.

公开(公告)号：US10776670B2

公开(公告)日：2020-09-15

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: G06K9/00 ,  G06K9/62 ,  G06K9/68 ,  G06N3/04 ,  G06N3/08

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：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：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：US20240177002A1

公开(公告)日：2024-05-30

申请号：US18497931

申请日：2023-10-30

Applicant: DeepMind Technologies Limited

Inventor： Timothy Paul Lillicrap ,  Jonathan James Hunt ,  Alexander Pritzel ,  Nicolas Manfred Otto Heess ,  Tom Erez ,  Yuval Tassa ,  David Silver ,  Daniel Pieter Wierstra

IPC: G06N3/08 ,  G06N3/006 ,  G06N3/045 ,  G06N3/084

CPC classification number: G06N3/08 ,  G06N3/006 ,  G06N3/045 ,  G06N3/084

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an actor neural network used to select actions to be performed by an agent interacting with an environment. One of the methods includes obtaining a minibatch of experience tuples; and updating current values of the parameters of the actor neural network, comprising: for each experience tuple in the minibatch: processing the training observation and the training action in the experience tuple using a critic neural network to determine a neural network output for the experience tuple, and determining a target neural network output for the experience tuple; updating current values of the parameters of the critic neural network using errors between the target neural network outputs and the neural network outputs; and updating the current values of the parameters of the actor neural network using the critic neural network.

公开(公告)号：US11983634B2

公开(公告)日：2024-05-14

申请号：US17486842

申请日：2021-09-27

Applicant: DeepMind Technologies Limited

Inventor： Razvan Pascanu ,  Raia Thais Hadsell ,  Victor Constant Bapst ,  Wojciech Czarnecki ,  James Kirkpatrick ,  Yee Whye Teh ,  Nicolas Manfred Otto Heess

IPC: G06N3/08 ,  G06N3/084 ,  G06N3/10 ,  G06N5/043

CPC classification number: G06N3/084 ,  G06N3/10 ,  G06N5/043

Abstract: A method is proposed for training a multitask computer system, such as a multitask neural network system. The system comprises a set of trainable workers and a shared module. The trainable workers and shared module are trained on a plurality of different tasks, such that each worker learns to perform a corresponding one of the tasks according to a respective task policy, and said shared policy network learns a multitask policy which represents common behavior for the tasks. The coordinated training is performed by optimizing an objective function comprising, for each task: a reward term indicative of an expected reward earned by a worker in performing the corresponding task according to the task policy; and at least one entropy term which regularizes the distribution of the task policy towards the distribution of the multitask policy.

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：US20230330848A1

公开(公告)日：2023-10-19

申请号：US18306711

申请日：2023-04-25

Applicant: DeepMind Technologies Limited

Inventor： Saran Tunyasuvunakool ,  Yuke Zhu ,  Joshua Merel ,  János Kramár ,  Ziyu Wang ,  Nicolas Manfred Otto Heess

IPC: B25J9/16 ,  G06N3/08 ,  G06N3/008 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045

CPC classification number: B25J9/163 ,  G06N3/08 ,  B25J9/161 ,  B25J9/1697 ,  G06N3/008 ,  G06N3/084 ,  G06N3/044 ,  G06N3/045

Abstract: A neural network control system for controlling an agent to perform a task in a real-world environment, operates based on both image data and proprioceptive data describing the configuration of the agent. The training of the control system includes both imitation learning, using datasets generated from previous performances of the task, and reinforcement learning, based on rewards calculated from control data output by the control system.

公开(公告)号：US11210585B1

公开(公告)日：2021-12-28

申请号：US15594228

申请日：2017-05-12

Applicant: DeepMind Technologies Limited

Inventor： Nicolas Manfred Otto Heess ,  Timothy Paul Lillicrap ,  Gregory Duncan Wayne ,  Yuval Tassa

IPC: G06N3/08 ,  G06N3/00

Abstract: Methods, systems, and apparatus for selecting actions to be performed by an agent interacting with an environment. One system includes a high-level controller neural network, low-level controller network, and subsystem. The high-level controller neural network receives an input observation and processes the input observation to generate a high-level output defining a control signal for the low-level controller. The low-level controller neural network receives a designated component of an input observation and processes the designated component and an input control signal to generate a low-level output that defines an action to be performed by the agent in response to the input observation. The subsystem receives a current observation characterizing a current state of the environment, determines whether criteria are satisfied for generating a new control signal, and based on the determination, provides appropriate inputs to the high-level and low-level controllers for selecting an action to be performed by the agent.

公开(公告)号：US20200293862A1

公开(公告)日：2020-09-17

申请号：US16885918

申请日：2020-05-28

Applicant: DeepMind Technologies Limited

Inventor： Ziyu Wang ,  Nicolas Manfred Otto Heess ,  Victor Constant Bapst

IPC: G06N3/04 ,  G06N3/08 ,  G06N3/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network. One of the methods includes maintaining a replay memory that stores trajectories generated as a result of interaction of an agent with an environment; and training an action selection neural network having policy parameters on the trajectories in the replay memory, wherein training the action selection neural network comprises: sampling a trajectory from the replay memory; and adjusting current values of the policy parameters by training the action selection neural network on the trajectory using an off-policy actor critic reinforcement learning technique.