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    PROGRAMMABLE REINFORCEMENT LEARNING SYSTEMS
    2.
    发明申请
    PROGRAMMABLE REINFORCEMENT LEARNING SYSTEMS 有权

    公开(公告)号：US20240394504A1

    公开(公告)日：2024-11-28

    申请号：US18637279

    申请日：2024-04-16

    Applicant: DeepMind Technologies Limited

    Inventor： Misha Man Ray Denil ,  Sergio Gomez Colmenarejo ,  Serkan Cabi ,  David William Saxton ,  Joao Ferdinando Gomes de Freitas

    IPC: G06N3/006 ,  G06F18/21 ,  G06F18/2451 ,  G06N3/045 ,  G06N3/047 ,  G06N3/084

    Abstract: A reinforcement learning system is proposed comprising a plurality of property detector neural networks. Each property detector neural network is arranged to receive data representing an object within an environment, and to generate property data associated with a property of the object. A processor is arranged to receive an instruction indicating a task associated with an object having an associated property, and process the output of the plurality of property detector neural networks based upon the instruction to generate a relevance data item. The relevance data item indicates objects within the environment associated with the task. The processor also generates a plurality of weights based upon the relevance data item, and, based on the weights, generates modified data representing the plurality of objects within the environment. A neural network is arranged to receive the modified data and to output an action associated with the task.

    PROGRAMMABLE REINFORCEMENT LEARNING SYSTEMS
    5.
    发明申请
    PROGRAMMABLE REINFORCEMENT LEARNING SYSTEMS 审中-公开

    公开(公告)号：US20200167633A1

    公开(公告)日：2020-05-28

    申请号：US16615061

    申请日：2018-05-22

    Applicant: DEEPMIND TECHNOLOGIES LIMITED

    Inventor： Misha Man Ray Denil ,  Sergio Gomez Colmenarejo ,  Serkan Cabi ,  David William Saxton ,  Joao Ferdinando Gomes de Freitas

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

    Abstract: A reinforcement learning system is proposed comprising a plurality of property detector neural networks. Each property detector neural network is arranged to receive data representing an object within an environment, and to generate property data associated with a property of the object. A processor is arranged to receive an instruction indicating a task associated with an object having an associated property, and process the output of the plurality of property detector neural networks based upon the instruction to generate a relevance data item. The relevance data item indicates objects within the environment associated with the task. The processor also generates a plurality of weights based upon the relevance data item, and, based on the weights, generates modified data representing the plurality of objects within the environment. A neural network is arranged to receive the modified data and to output an action associated with the task.

    TRAINING A NEURAL NETWORK TO CONTROL AN AGENT USING TASK-RELEVANT ADVERSARIAL IMITATION LEARNING
    9.
    发明申请
    TRAINING A NEURAL NETWORK TO CONTROL AN AGENT USING TASK-RELEVANT ADVERSARIAL IMITATION LEARNING 有权

    公开(公告)号：US20220261639A1

    公开(公告)日：2022-08-18

    申请号：US17625361

    申请日：2020-07-16

    Applicant: DeepMind Technologies Limited

    Inventor： Konrad Zolna ,  Scott Ellison Reed ,  Ziyu Wang ,  Alexander Novikov ,  Sergio Gomez Colmenarejo ,  Joao Ferdinando Gomes de Freitas ,  David Budden ,  Serkan Cabi

    IPC: G06N3/08

    Abstract: A method is proposed of training a neural network to generate action data for controlling an agent to perform a task in an environment. The method includes obtaining, for each of a plurality of performances of the task, one or more first tuple datasets, each first tuple dataset comprising state data characterizing a state of the environment at a corresponding time during the performance of the task; and a concurrent process of training the neural network and a discriminator network. The training process comprises a plurality of neural network update steps and a plurality of discriminator network update steps. Each neural network update step comprises: receiving state data characterizing a current state of the environment; using the neural network and the state data to generate action data indicative of an action to be performed by the agent; forming a second tuple dataset comprising the state data; using the second tuple dataset to generate a reward value, wherein the reward value comprises an imitation value generated by the discriminator network based on the second tuple dataset; and updating one or more parameters of the neural network based on the reward value. Each discriminator network update step comprises updating the discriminator network based on a plurality of the first tuple datasets and a plurality of the second tuple datasets, the update being to increase respective imitation values which the discriminator network generates upon receiving any of the plurality of the first tuple datasets compared to respective imitation values which the discriminator network generates upon receiving any of the plurality of the second tuple datasets. The updating process is performed subject to a constraint that the updated discriminator network, upon receiving any of at least a certain proportion of a first subset of the first tuple datasets and/or any of at least a certain proportion of a second subset of the second tuple datasets, does not generate imitation values which correctly indicate that those tuple datasets are first or second tuple datasets.

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