Patents by Inventor Ashique Rupam Mahmood

Ashique Rupam Mahmood 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).

  • Real-time real-world reinforcement learning systems and methods
    Patent number: 12005578
    Abstract: A reinforcement learning architecture for facilitating reinforcement learning in connection with operation of an external real-time system that includes a plurality of devices operating in a real-world environment. The reinforcement learning architecture includes a plurality of communicators, a task manager, and a reinforcement learning agent that interact with each other to effectuate a policy for achieving a defined objective in the real-world environment. Each of the communicators receives sensory data from a corresponding device and the task manager generates a joint state vector based on the sensory data. The reinforcement learning agent generates, based on the joint state vector, a joint action vector, which the task manager parses into a plurality of actuation commands. The communicators transmit the actuation commands to the plurality of devices in the real-world environment.
    Type: Grant
    Filed: September 4, 2019
    Date of Patent: June 11, 2024
    Assignee: Ocado Innovations Limited
    Inventors: Ashique Rupam Mahmood, Brent J. Komer, Dmytro Korenkevych
  • REAL-TIME REAL-WORLD REINFORCEMENT LEARNING SYSTEMS AND METHODS
    Publication number: 20200074241
    Abstract: A reinforcement learning architecture for facilitating reinforcement learning in connection with operation of an external real-time system that includes a plurality of devices operating in a real-world environment. The reinforcement learning architecture includes a plurality of communicators, a task manager, and a reinforcement learning agent that interact with each other to effectuate a policy for achieving a defined objective in the real-world environment. Each of the communicators receives sensory data from a corresponding device and the task manager generates a joint state vector based on the sensory data. The reinforcement learning agent generates, based on the joint state vector, a joint action vector, which the task manager parses into a plurality of actuation commands. The communicators transmit the actuation commands to the plurality of devices in the real-world environment.
    Type: Application
    Filed: September 4, 2019
    Publication date: March 5, 2020
    Inventors: Ashique Rupam Mahmood, Brent J. Komer, Dmytro Korenkevych