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    Training action selection neural networks using off-policy actor critic reinforcement learning
    31.
    发明授权
    Training action selection neural networks using off-policy actor critic reinforcement learning 有权

    公开(公告)号：US10706352B2

    公开(公告)日：2020-07-07

    申请号：US16402687

    申请日：2019-05-03

    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.

    Data-efficient reinforcement learning for continuous control tasks
    32.
    发明授权
    Data-efficient reinforcement learning for continuous control tasks 有权

    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.

    TRAINING AN ACTION SELECTION SYSTEM USING RELATIVE ENTROPY Q-LEARNING
    38.
    发明公开
    TRAINING AN ACTION SELECTION SYSTEM USING RELATIVE ENTROPY Q-LEARNING 审中-公开

    公开(公告)号：US20230214649A1

    公开(公告)日：2023-07-06

    申请号：US18008838

    申请日：2021-07-27

    Applicant: DeepMind Technologies Limited

    Inventor： Rae Chan Jeong ,  Jost Tobias Springenberg ,  Jacqueline Ok-chan Kay ,  Daniel Hai Huan Zheng ,  Alexandre Galashov ,  Nicolas Manfred Otto Heess ,  Francesco Nori

    IPC: G06N3/08

    CPC classification number: G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection system using reinforcement learning techniques. In one aspect, a method comprises at each of multiple iterations: obtaining a batch of experience, each experience tuple comprising: a first observation, an action, a second observation, and a reward; for each experience tuple, determining a state value for the second observation, comprising: processing the first observation using a policy neural network to generate an action score for each action in a set of possible actions; sampling multiple actions from the set of possible actions in accordance with the action scores; processing the second observation using a Q neural network to generate a Q value for each sampled action; and determining the state value for the second observation; and determining an update to current values of the Q neural network parameters using the state values.

    Neural networks for selecting actions to be performed by a robotic agent
    39.
    发明授权
    Neural networks for selecting actions to be performed by a robotic agent 有权

    公开(公告)号：US11534911B2

    公开(公告)日：2022-12-27

    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: G06N3/08 ,  B25J9/16 ,  G06N3/04 ,  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.

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