Invention Application
- Patent Title: PROGRAMMABLE REINFORCEMENT LEARNING SYSTEMS
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Application No.: US16615061Application Date: 2018-05-22
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Publication No.: US20200167633A1Publication Date: 2020-05-28
- Inventor: Misha Man Ray Denil , Sergio Gomez Colmenarejo , Serkan Cabi , David William Saxton , Joao Ferdinando Gomes de Freitas
- Applicant: DEEPMIND TECHNOLOGIES LIMITED
- International Application: PCT/EP2018/063306 WO 20180522
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/08 ; G06K9/62

Abstract:
A reinforcement learning system is proposed comprising a plurality of property detector neural networks. Each property detector neural network is arranged to receive data representing an object within an environment, and to generate property data associated with a property of the object. A processor is arranged to receive an instruction indicating a task associated with an object having an associated property, and process the output of the plurality of property detector neural networks based upon the instruction to generate a relevance data item. The relevance data item indicates objects within the environment associated with the task. The processor also generates a plurality of weights based upon the relevance data item, and, based on the weights, generates modified data representing the plurality of objects within the environment. A neural network is arranged to receive the modified data and to output an action associated with the task.
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