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    MULTI-AGENT REINFORCEMENT LEARNING WITH MATCHMAKING POLICIES
    42.
    发明公开
    MULTI-AGENT REINFORCEMENT LEARNING WITH MATCHMAKING POLICIES 审中-公开

    公开(公告)号：US20230244936A1

    公开(公告)日：2023-08-03

    申请号：US18131567

    申请日：2023-04-06

    Applicant: DeepMind Technologies Limited

    Inventor： David Silver ,  Oriol Vinyals ,  Maxwell Elliot Jaderberg

    IPC: G06N3/08 ,  H04L9/40 ,  G06F18/214

    CPC classification number: G06N3/08 ,  H04L63/205 ,  G06F18/214

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network having a plurality of policy parameters and used to select actions to be performed by an agent to control the agent to perform a particular task while interacting with one or more other agents in an environment. In one aspect, the method includes: maintaining data specifying a pool of candidate action selection policies; maintaining data specifying respective matchmaking policy; and training the policy neural network using a reinforcement learning technique to update the policy parameters. The policy parameters define policies to be used in controlling the agent to perform the particular task.

    Training action selection neural networks using a differentiable credit function
    43.
    发明授权
    Training action selection neural networks using a differentiable credit function 有权

    公开(公告)号：US11651208B2

    公开(公告)日：2023-05-16

    申请号：US16615042

    申请日：2018-05-22

    Applicant: DEEPMIND TECHNOLOGIES LIMITED

    Inventor： Zhongwen Xu ,  Hado Phillip van Hasselt ,  Joseph Varughese Modayil ,  Andre da Motta Salles Barreto ,  David Silver

    IPC: G06N3/08 ,  G06N3/04

    CPC classification number: G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning. A reinforcement learning neural network selects actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The reinforcement learning neural network has at least one input to receive an input observation characterizing a state of the environment and at least one output for determining an action to be performed by the agent in response to the input observation. The system includes a reward function network coupled to the reinforcement learning neural network. The reward function network has an input to receive reward data characterizing a reward provided by one or more states of the environment and is configured to determine a reward function to provide one or more target values for training the reinforcement learning neural network.

    Multi-agent reinforcement learning with matchmaking policies
    45.
    发明授权
    Multi-agent reinforcement learning with matchmaking policies 有权

    公开(公告)号：US11627165B2

    公开(公告)日：2023-04-11

    申请号：US16752496

    申请日：2020-01-24

    Applicant: DeepMind Technologies Limited

    Inventor： David Silver ,  Oriol Vinyals ,  Maxwell Elliot Jaderberg

    IPC: G06N3/08 ,  H04L9/40 ,  G06K9/62

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network having a plurality of policy parameters and used to select actions to be performed by an agent to control the agent to perform a particular task while interacting with one or more other agents in an environment. In one aspect, the method includes: maintaining data specifying a pool of candidate action selection policies; maintaining data specifying respective matchmaking policy; and training the policy neural network using a reinforcement learning technique to update the policy parameters. The policy parameters define policies to be used in controlling the agent to perform the particular task.

    Training neural networks using a prioritized experience memory
    46.
    发明授权
    Training neural networks using a prioritized experience memory 有权

    公开(公告)号：US11568250B2

    公开(公告)日：2023-01-31

    申请号：US16866365

    申请日：2020-05-04

    Applicant: DeepMind Technologies Limited

    Inventor： Tom Schaul ,  John Quan ,  David Silver

    IPC: G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network used to select actions performed by a reinforcement learning agent interacting with an environment. In one aspect, a method includes maintaining a replay memory, where the replay memory stores pieces of experience data generated as a result of the reinforcement learning agent interacting with the environment. Each piece of experience data is associated with a respective expected learning progress measure that is a measure of an expected amount of progress made in the training of the neural network if the neural network is trained on the piece of experience data. The method further includes selecting a piece of experience data from the replay memory by prioritizing for selection pieces of experience data having relatively higher expected learning progress measures and training the neural network on the selected piece of experience data.

    Training action selection neural networks using look-ahead search
    47.
    发明授权
    Training action selection neural networks using look-ahead search 有权

    公开(公告)号：US11449750B2

    公开(公告)日：2022-09-20

    申请号：US16617478

    申请日：2018-05-28

    Applicant: DEEPMIND TECHNOLOGIES LIMITED

    Inventor： Karen Simonyan ,  David Silver ,  Julian Schrittwieser

    IPC: G06N3/08 ,  G06N7/00

    Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media, for training an action selection neural network. One of the methods includes receiving an observation characterizing a current state of the environment; determining a target network output for the observation by performing a look ahead search of possible future states of the environment starting from the current state until the environment reaches a possible future state that satisfies one or more termination criteria, wherein the look ahead search is guided by the neural network in accordance with current values of the network parameters; selecting an action to be performed by the agent in response to the observation using the target network output generated by performing the look ahead search; and storing, in an exploration history data store, the target network output in association with the observation for use in updating the current values of the network parameters.

    MULTI-AGENT REINFORCEMENT LEARNING WITH MATCHMAKING POLICIES
    50.
    发明申请
    MULTI-AGENT REINFORCEMENT LEARNING WITH MATCHMAKING POLICIES 审中-公开

    公开(公告)号：US20200244707A1

    公开(公告)日：2020-07-30

    申请号：US16752496

    申请日：2020-01-24

    Applicant: DeepMind Technologies Limited

    Inventor： David Silver ,  Oriol Vinyals ,  Maxwell Elliot Jaderberg

    IPC: H04L29/06 ,  G06N3/08 ,  G06K9/62

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network having a plurality of policy parameters and used to select actions to be performed by an agent to control the agent to perform a particular task while interacting with one or more other agents in an environment. In one aspect, the method includes: maintaining data specifying a pool of candidate action selection policies; maintaining data specifying respective matchmaking policy; and training the policy neural network using a reinforcement learning technique to update the policy parameters. The policy parameters define policies to be used in controlling the agent to perform the particular task.

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