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    Meta-gradient updates for training return functions for reinforcement learning systems
    22.
    发明授权
    Meta-gradient updates for training return functions for reinforcement learning systems 有权

    公开(公告)号：US10860926B2

    公开(公告)日：2020-12-08

    申请号：US16417536

    申请日：2019-05-20

    Applicant: DeepMind Technologies Limited

    Inventor： Zhongwen Xu ,  Hado Philip van Hasselt ,  David Silver

    IPC: G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for reinforcement learning. The embodiments described herein apply meta-learning (and in particular, meta-gradient reinforcement learning) to learn an optimum return function G so that the training of the system is improved. This provides a more effective and efficient means of training a reinforcement learning system as the system is able to converge on an optimum set of one or more policy parameters θ more quickly by training the return function G as it goes. In particular, the return function G is made dependent on the one or more policy parameters θ and a meta-objective function J′ is used that is differentiated with respect to the one or more return parameters η to improve the training of the return function G.

    Continuous control with deep reinforcement learning
    23.
    发明授权
    Continuous control with deep reinforcement learning 有权

    公开(公告)号：US10776692B2

    公开(公告)日：2020-09-15

    申请号：US15217758

    申请日：2016-07-22

    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 ,  G06N3/00 ,  G06N3/04

    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.

    ENVIRONMENT PREDICTION USING REINFORCEMENT LEARNING
    25.
    发明申请
    ENVIRONMENT PREDICTION USING REINFORCEMENT LEARNING 审中-公开

    公开(公告)号：US20190259051A1

    公开(公告)日：2019-08-22

    申请号：US16403314

    申请日：2019-05-03

    Applicant: DeepMind Technologies Limited

    Inventor： David Silver ,  Tom Schaul ,  Matteo Hessel ,  Hado Philip van Hasselt

    IPC: G06Q30/02 ,  G06N3/08

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for prediction of an outcome related to an environment. In one aspect, a system comprises a state representation neural network that is configured to: receive an observation characterizing a state of an environment being interacted with by an agent and process the observation to generate an internal state representation of the environment state; a prediction neural network that is configured to receive a current internal state representation of a current environment state and process the current internal state representation to generate a predicted subsequent state representation of a subsequent state of the environment and a predicted reward for the subsequent state; and a value prediction neural network that is configured to receive a current internal state representation of a current environment state and process the current internal state representation to generate a value prediction.

    REINFORCEMENT LEARNING WITH AUXILIARY TASKS
    26.
    发明申请
    REINFORCEMENT LEARNING WITH AUXILIARY TASKS 审中-公开

    公开(公告)号：US20190258938A1

    公开(公告)日：2019-08-22

    申请号：US16403385

    申请日：2019-05-03

    Applicant: DeepMind Technologies Limited

    Inventor： Volodymyr Mnih ,  Wojciech Czarnecki ,  Maxwell Elliot Jaderberg ,  Tom Schaul ,  David Silver ,  Koray Kavukcuoglu

    IPC: G06N3/08 ,  G06N20/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The method includes: training an action selection policy neural network, and during the training of the action selection neural network, training one or more auxiliary control neural networks and a reward prediction neural network. Each of the auxiliary control neural networks is configured to receive a respective intermediate output generated by the action selection policy neural network and generate a policy output for a corresponding auxiliary control task. The reward prediction neural network is configured to receive one or more intermediate outputs generated by the action selection policy neural network and generate a corresponding predicted reward. Training each of the auxiliary control neural networks and the reward prediction neural network comprises adjusting values of the respective auxiliary control parameters, reward prediction parameters, and the action selection policy network parameters.

    Training action selection neural networks using look-ahead search
    28.
    发明授权
    Training action selection neural networks using look-ahead search 有权

    公开(公告)号：US12147899B2

    公开(公告)日：2024-11-19

    申请号：US18528640

    申请日：2023-12-04

    Applicant: DeepMind Technologies Limited

    Inventor： Karen Simonyan ,  David Silver ,  Julian Schrittwieser

    IPC: G06N3/08 ,  G06N7/01

    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
    29.
    发明申请
    MULTI-AGENT REINFORCEMENT LEARNING WITH MATCHMAKING POLICIES 有权

    公开(公告)号：US20240370725A1

    公开(公告)日：2024-11-07

    申请号：US18771770

    申请日：2024-07-12

    Applicant: DeepMind Technologies Limited

    Inventor： David Silver ,  Oriol Vinyals ,  Maxwell Elliot Jaderberg

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

    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
    30.
    发明授权
    Training neural networks using a prioritized experience memory 有权

    公开(公告)号：US12086714B2

    公开(公告)日：2024-09-10

    申请号：US18103416

    申请日：2023-01-30

    Applicant: DeepMind Technologies Limited

    Inventor： Tom Schaul ,  John Quan ,  David Silver

    IPC: G06N3/08 ,  G06N3/088

    CPC classification number: G06N3/08 ,  G06N3/088 ,  Y04S10/50

    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.

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