Patents by Inventor Maximilian IGL

Maximilian IGL 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).

  • GENERALIZED REINFORCEMENT LEARNING AGENT
    Publication number: 20230111659
    Abstract: An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.
    Type: Application
    Filed: November 2, 2022
    Publication date: April 13, 2023
    Inventors: Sam Michael DEVLIN, Maximilian IGL, Kamil Andrzej CIOSEK, Yingzhen LI, Sebastian TSCHIATSCHEK, Cheng ZHANG, Katja HOFMANN
  • GENERATING SIMULATED AGENT TRAJECTORIES USING PARALLEL BEAM SEARCH
    Publication number: 20230082365
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating simulated trajectories using parallel beam search.
    Type: Application
    Filed: September 16, 2022
    Publication date: March 16, 2023
    Inventors: Kyriacos Christoforos Shiarlis, Dragomir Anguelov, Brandyn Allen White, Shimon Azariah Whiteson, Maximilian Igl, Daewoo Kim, Alex Richard Kuefler, Paul Marie Vincent Mougin, Punit Nilesh Shah, Mark Palatucci
  • Generalized reinforcement learning agent
    Patent number: 11526812
    Abstract: An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.
    Type: Grant
    Filed: October 1, 2019
    Date of Patent: December 13, 2022
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Sam Michael Devlin, Maximilian Igl, Kamil Andrzej Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Katja Hofmann
  • GENERALIZED REINFORCEMENT LEARNING AGENT
    Publication number: 20210097445
    Abstract: An apparatus has a memory storing a reinforcement learning policy with an optimization component and a data collection component. The apparatus has a regularization component which applies regularization selectively between the optimization component of the reinforcement learning policy and the data collection component of the reinforcement learning policy. A processor carries out a reinforcement learning process by: triggering execution of an agent according to the policy and with respect to a first task; observing values of variables comprising: an observation space of the agent, an action of the agent; and updating the policy using reinforcement learning according to the observed values and taking into account the regularization.
    Type: Application
    Filed: October 1, 2019
    Publication date: April 1, 2021
    Inventors: Sam Michael DEVLIN, Maximilian IGL, Kamil Andrzej CIOSEK, Yingzhen LI, Sebastian TSCHIATSCHEK, Cheng ZHANG, Katja HOFMANN