Patents by Inventor Adria Puigdomenech Badia

Adria Puigdomenech Badia 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).

  • Publication number: 20190303764
    Abstract: A method includes maintaining respective episodic memory data for each of multiple actions; receiving a current observation characterizing a current state of an environment being interacted with by an agent; processing the current observation using an embedding neural network in accordance with current values of parameters of the embedding neural network to generate a current key embedding for the current observation; for each action of the plurality of actions: determining the p nearest key embeddings in the episodic memory data for the action to the current key embedding according to a distance measure, and determining a Q value for the action from the return estimates mapped to by the p nearest key embeddings in the episodic memory data for the action; and selecting, using the Q values for the actions, an action from the multiple actions as the action to be performed by the agent.
    Type: Application
    Filed: June 19, 2019
    Publication date: October 3, 2019
    Inventors: Benigno Uria-Martínez, Alexander Pritzel, Charles Blundell, Adria Puigdomenech Badia
  • Publication number: 20190258929
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.
    Type: Application
    Filed: May 3, 2019
    Publication date: August 22, 2019
    Inventors: Volodymyr Mnih, Adria Puigdomenech Badia, Alexander Benjamin Graves, Timothy James Alexander Harley, David Silver, Koray Kavukcuoglu
  • Patent number: 10346741
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.
    Type: Grant
    Filed: May 11, 2018
    Date of Patent: July 9, 2019
    Assignee: DeepMind Technologies Limited
    Inventors: Volodymyr Mnih, Adrià Puigdomènech Badia, Alexander Benjamin Graves, Timothy James Alexander Harley, David Silver, Koray Kavukcuoglu
  • Publication number: 20180260708
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.
    Type: Application
    Filed: May 11, 2018
    Publication date: September 13, 2018
    Inventors: Volodymyr Mnih, Adrià Puigdomènech Badia, Alexander Benjamin Graves, Timothy James Alexander Harley, David Silver, Koray Kavukcuoglu
  • Publication number: 20170140270
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.
    Type: Application
    Filed: November 11, 2016
    Publication date: May 18, 2017
    Inventors: Volodymyr Mnih, Adrià Puigdomènech Badia, Alexander Benjamin Graves, Timothy James Alexander Harley, David Silver, Koray Kavukcuoglu