Patents by Inventor Andrew John Eakins

Andrew John Eakins 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).

  • Adversarial reinforcement learning for procedural content generation and improved generalization
    Patent number: 11883746
    Abstract: Methods, apparatus and systems are provided for training a first reinforcement-learning (RL) agent and a second RL agent coupled to a computer game environment using RL techniques. The first RL agent iteratively generates a sub-goal sequence in relation to an overall goal within the computer game environment, where the first RL agent generates a new sub-goal for the sub-goal sequence after a second RL agent, interacting with the computer game environment, successfully achieves a current sub-goal in the sub-goal sequence. The second RL agent iteratively interacts with the computer game environment to achieve the current sub-goal in which each iterative interaction includes an attempt by the second RL agent for interacting with the computer game environment to achieve the current sub-goal. The first RL agent is updated using a first reward issued when the second RL agent successfully achieves the current sub-goal.
    Type: Grant
    Filed: September 17, 2021
    Date of Patent: January 30, 2024
    Assignee: ELECTRONIC ARTS INC.
    Inventors: Linus Mathias Gisslén, Andrew John Eakins
  • Adversarial Reinforcement Learning for Procedural Content Generation and Improved Generalization
    Publication number: 20240017175
    Abstract: Methods, apparatus and systems are provided for training a first reinforcement-learning (RL) agent and a second RL agent coupled to a computer game environment using RL techniques. The first RL agent iteratively generates a sub-goal sequence in relation to an overall goal within the computer game environment, where the first RL agent generates a new sub-goal for the sub-goal sequence after a second RL agent, interacting with the computer game environment, successfully achieves a current sub-goal in the sub-goal sequence. The second RL agent iteratively interacts with the computer game environment to achieve the current sub-goal in which each iterative interaction includes an attempt by the second RL agent for interacting with the computer game environment to achieve the current sub-goal. The first RL agent is updated using a first reward issued when the second RL agent successfully achieves the current sub-goal.
    Type: Application
    Filed: September 26, 2023
    Publication date: January 18, 2024
    Inventors: Linus Mathias Gisslén, Andrew John Eakins
  • Adversarial Reinforcement Learning for Procedural Content Generation and Improved Generalization
    Publication number: 20220266145
    Abstract: Methods, apparatus and systems are provided for training a first reinforcement-learning (RL) agent and a second RL agent coupled to a computer game environment using RL techniques. The first RL agent iteratively generates a sub-goal sequence in relation to an overall goal within the computer game environment, where the first RL agent generates a new sub-goal for the sub-goal sequence after a second RL agent, interacting with the computer game environment, successfully achieves a current sub-goal in the sub-goal sequence. The second RL agent iteratively interacts with the computer game environment to achieve the current sub-goal in which each iterative interaction includes an attempt by the second RL agent for interacting with the computer game environment to achieve the current sub-goal. The first RL agent is updated using a first reward issued when the second RL agent successfully achieves the current sub-goal.
    Type: Application
    Filed: September 17, 2021
    Publication date: August 25, 2022
    Inventors: Linus Mathias Gisslén, Andrew John Eakins