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    CONTROLLING REINFORCEMENT LEARNING AGENTS USING GEOMETRIC POLICY COMPOSITION
    1.
    发明申请
    CONTROLLING REINFORCEMENT LEARNING AGENTS USING GEOMETRIC POLICY COMPOSITION 有权

    公开(公告)号：US20250124297A1

    公开(公告)日：2025-04-17

    申请号：US18834208

    申请日：2023-01-30

    Applicant: DeepMind Technologies Limited

    Inventor： Mark Daniel Rowland ,  Shantanu Yogeshraj Thakoor ,  Andre da Motta Salles Barreto ,  Diana Luiza Borsa ,  William Clinton Dabney ,  Remi Munos

    IPC: G06N3/092 ,  G06N3/0455

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling a reinforcement learning agent in an environment. One of the methods may include maintaining data specifying a base policy set comprising a plurality of base policies for controlling the agent; receiving a current observation characterizing a current state of the environment; generating, for each of the plurality of base policies, one or more predicted future observations characterizing respective future states of the environment that are subsequent to the current state of the environment; using the predicted future observations generated for the plurality of base policies to determine a respective estimated value for each composite policy in a composite policy set with respect to the current state of the environment; and selecting an action using the respective estimated values for the composite policies.

    REINFORCEMENT LEARNING USING PSEUDO-COUNTS
    2.
    发明申请
    REINFORCEMENT LEARNING USING PSEUDO-COUNTS 审中-公开

    公开(公告)号：US20200327405A1

    公开(公告)日：2020-10-15

    申请号：US16303501

    申请日：2017-05-18

    Applicant: DEEPMIND TECHNOLOGIES LIMITED

    Inventor： Marc Gendron-Bellemare ,  Remi Munos ,  Srinivasan Sriram

    IPC: G06N3/08 ,  G06N3/04 ,  G06F17/18

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network used to select actions to be performed by an agent interacting with an environment. One of the methods includes obtaining data identifying (i) a first observation characterizing a first state of the environment, (ii) an action performed by the agent in response to the first observation, and (iii) an actual reward received resulting from the agent performing the action in response to the first observation; determining a pseudo-count for the first observation; determining an exploration reward bonus that incentivizes the agent to explore the environment from the pseudo-count for the first observation; generating a combined reward from the actual reward and the exploration reward bonus; and adjusting current values of the parameters of the neural network using the combined reward.

    Training machine learning models using task selection policies to increase learning progress
    6.
    发明授权
    Training machine learning models using task selection policies to increase learning progress 有权

    公开(公告)号：US10936949B2

    公开(公告)日：2021-03-02

    申请号：US16508042

    申请日：2019-07-10

    Applicant: DeepMind Technologies Limited

    Inventor： Marc Gendron-Bellemare ,  Jacob Lee Menick ,  Alexander Benjamin Graves ,  Koray Kavukcuoglu ,  Remi Munos

    IPC: G06N3/08 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.

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