Invention Application
- Patent Title: PROGRAMMABLE REINFORCEMENT LEARNING SYSTEMS
-
Application No.: US18637279Application Date: 2024-04-16
-
Publication No.: US20240394504A1Publication Date: 2024-11-28
- Inventor: Misha Man Ray Denil , Sergio Gomez Colmenarejo , Serkan Cabi , David William Saxton , Joao Ferdinando Gomes de Freitas
- Applicant: DeepMind Technologies Limited
- Applicant Address: GB London
- Assignee: DeepMind Technologies Limited
- Current Assignee: DeepMind Technologies Limited
- Current Assignee Address: GB London
- Main IPC: G06N3/006
- IPC: G06N3/006 ; G06F18/21 ; G06F18/2451 ; G06N3/045 ; G06N3/047 ; G06N3/084

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.
Information query