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US Patent 12001320 Cloud architecture for reinforcement learning

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Patent
Patent
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Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
120013200
Patent Inventor Names
Kashyap Maheshkumar Patel0
Taylor Paul Spangler0
Marc Mas Mezquita0
Steve K. Lim0
Levent Ozgur0
Timothy James Chapman0
Xiaoyan Liu0
Date of Patent
June 4, 2024
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Patent Application Number
178554340
Date Filed
June 30, 2022
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Patent Citations
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US Patent 10346741 Asynchronous deep reinforcement learning
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US Patent 10733501 Environment prediction using reinforcement learning
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US Patent 10802864 Modular reinforcement-learning-based application manager
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US Patent 11465650 Model-free reinforcement learning
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US Patent 11669753 Artificial intelligence system providing interactive model interpretation and enhancement tools
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US Patent 11775817 Reinforcement learning-based techniques for training a natural media agent
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US Patent 11790239 Deep learning testing
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US Patent 11889819 Methods and apparatus to adaptively optimize feed blend and medicinal selection using machine learning to optimize animal milk production and health
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Patent Primary Examiner
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Qamrun Nahar
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Patent abstract

The technology described herein provides a cloud reinforcement-learning architecture that allows a single reinforcement-learning model to interact with multiple live software environments. The live software environments and the single reinforcement-learning model run in a distributed computing environment (e.g., cloud environment). The single reinforcement-learning model may run on a first computing device(s) with a graphical processing unit (GPU) to aid in training the single reinforcement-learning model. At a high level, the single reinforcement-learning model may receive state telemetry data from the multiple live environments. The single reinforcement-learning model selects an available action for each set of state telemetry data received and communicates the selection to appropriate the test agent. The test agent then facilitates completion of the action within the software instance being tested in the live environment. A reward is then determined for the action. The single reinforcement-learning model may then be trained.

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