Patents by Inventor Joshua Simon Abramson

Joshua Simon Abramson has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11769049
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network system used to control an agent interacting with an environment to perform a specified task. One of the methods includes causing the agent to perform a task episode in which the agent attempts to perform the specified task; for each of one or more particular time steps in the sequence: generating a modified reward for the particular time step from (i) the actual reward at the time step and (ii) value predictions at one or more time steps that are more than a threshold number of time steps after the particular time step in the sequence; and training, through reinforcement learning, the neural network system using at least the modified rewards for the particular time steps.
    Type: Grant
    Filed: September 28, 2020
    Date of Patent: September 26, 2023
    Assignee: DeepMind Technologies Limited
    Inventors: Gregory Duncan Wayne, Timothy Paul Lillicrap, Chia-Chun Hung, Joshua Simon Abramson
  • Publication number: 20230178076
    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an interactive agent can be controlled based on multi-modal inputs that include both an observation image and a natural language text sequence.
    Type: Application
    Filed: December 7, 2022
    Publication date: June 8, 2023
    Inventors: Joshua Simon Abramson, Arun Ahuja, Federico Javier Carnevale, Petko Ivanov Georgiev, Chia-Chun Hung, Timothy Paul Lillicrap, Alistair Michael Muldal, Adam Anthony Santoro, Tamara Louise von Glehn, Jessica Paige Landon, Gregory Duncan Wayne, Chen Yan, Rui Zhu
  • Publication number: 20210081723
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network system used to control an agent interacting with an environment to perform a specified task. One of the methods includes causing the agent to perform a task episode in which the agent attempts to perform the specified task; for each of one or more particular time steps in the sequence: generating a modified reward for the particular time step from (i) the actual reward at the time step and (ii) value predictions at one or more time steps that are more than a threshold number of time steps after the particular time step in the sequence; and training, through reinforcement learning, the neural network system using at least the modified rewards for the particular time steps.
    Type: Application
    Filed: September 28, 2020
    Publication date: March 18, 2021
    Inventors: Gregory Duncan Wayne, Timothy Paul Lillicrap, Chia-Chun Hung, Joshua Simon Abramson
  • Patent number: 10789511
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network system used to control an agent interacting with an environment to perform a specified task. One of the methods includes causing the agent to perform a task episode in which the agent attempts to perform the specified task; for each of one or more particular time steps in the sequence: generating a modified reward for the particular time step from (i) the actual reward at the time step and (ii) value predictions at one or more time steps that are more than a threshold number of time steps after the particular time step in the sequence; and training, through reinforcement learning, the neural network system using at least the modified rewards for the particular time steps.
    Type: Grant
    Filed: October 14, 2019
    Date of Patent: September 29, 2020
    Assignee: DeepMind Technologies Limited
    Inventors: Gregory Duncan Wayne, Timothy Paul Lillicrap, Chia-Chun Hung, Joshua Simon Abramson
  • Publication number: 20200117956
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network system used to control an agent interacting with an environment to perform a specified task. One of the methods includes causing the agent to perform a task episode in which the agent attempts to perform the specified task; for each of one or more particular time steps in the sequence: generating a modified reward for the particular time step from (i) the actual reward at the time step and (ii) value predictions at one or more time steps that are more than a threshold number of time steps after the particular time step in the sequence; and training, through reinforcement learning, the neural network system using at least the modified rewards for the particular time steps.
    Type: Application
    Filed: October 14, 2019
    Publication date: April 16, 2020
    Inventors: Gregory Duncan Wayne, Timothy Paul Lillicrap, Chia-Chun Hung, Joshua Simon Abramson