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公开(公告)号：US11836596B2
公开(公告)日：2023-12-05
申请号：US17107621
申请日：2020-11-30
Applicant: DeepMind Technologies Limited
Inventor： Mike Chrzanowski , Jack William Rae , Ryan Faulkner , Theophane Guillaume Weber , David Nunes Raposo , Adam Anthony Santoro
IPC: G06N3/08 , G06N3/042 , G06N3/04 , G06N20/00 , G06F18/2413
CPC classification number: G06N3/042 , G06F18/24137 , G06N3/04 , G06N3/08 , G06N20/00
Abstract: A system including one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to implement a memory and memory-based neural network is described. The memory is configured to store a respective memory vector at each of a plurality of memory locations in the memory. The memory-based neural network is configured to: at each of a plurality of time steps: receive an input; determine an update to the memory, wherein determining the update comprising applying an attention mechanism over the memory vectors in the memory and the received input; update the memory using the determined update to the memory; and generate an output for the current time step using the updated memory.
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公开(公告)号：US20230196146A1
公开(公告)日：2023-06-22
申请号：US18168123
申请日：2023-02-13
Applicant: DeepMind Technologies Limited
Inventor： Yujia Li , Victor Constant Bapst , Vinicius Zambaldi , David Nunes Raposo , Adam Anthony Santoro
Abstract: A neural network system is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network is arranged to receive data output by the relational network, and use the received data to select a respective action.
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公开(公告)号：US20230101930A1
公开(公告)日：2023-03-30
申请号：US17794780
申请日：2021-02-08
Applicant: DeepMind Technologies Limited
Inventor： Samuel Ritter , Ryan Faulkner , David Nunes Raposo
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment to accomplish a goal. In one aspect, a method comprises: generating a respective planning embedding corresponding to each of multiple experience tuples in an external memory, wherein each experience tuple characterizes interaction of the agent with the environment at a respective previous time step; processing the planning embeddings using a planning neural network to generate an implicit plan for accomplishing the goal; and selecting the action to be performed by the agent at the time step using the implicit plan.
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Patent Agency Ranking
公开(公告)号：US20240086703A1
公开(公告)日：2024-03-14
申请号：US18275542
申请日：2022-02-04
Applicant: DeepMind Technologies Limited
Inventor： Samuel Ritter , David Nunes Raposo
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: A computer-implemented reinforcement learning neural network system that learns a model of rewards in order to relate actions by an agent in an environment to their long-term consequences. The model learns to decompose the rewards into components explainable by different past states. That is, the model learns to associate when being in a particular state of the environment is predictive of a reward in a later state, even when the later state, and reward, is only achieved after a very long time delay.
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公开(公告)号：US20190354885A1
公开(公告)日：2019-11-21
申请号：US16417580
申请日：2019-05-20
Applicant: DeepMind Technologies Limited
Inventor： Yujia Li , Victor Constant Bapst , Vinicius Zambaldi , David Nunes Raposo , Adam Anthony Santoro
Abstract: A neural network system is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network is arranged to receive data output by the relational network, and use the received data to select a respective action.
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公开(公告)号：US11580429B2
公开(公告)日：2023-02-14
申请号：US16417580
申请日：2019-05-20
Applicant: DeepMind Technologies Limited
Inventor： Yujia Li , Victor Constant Bapst , Vinicius Zambaldi , David Nunes Raposo , Adam Anthony Santoro
Abstract: A neural network system is proposed, including an input network for extracting, from state data, respective entity data for each a plurality of entities which are present, or at least potentially present, in the environment. The entity data describes the entity. The neural network contains a relational network for parsing this data, which includes one or more attention blocks which may be stacked to perform successive actions on the entity data. The attention blocks each include a respective transform network for each of the entities. The transform network for each entity is able to transform data which the transform network receives for the entity into modified entity data for the entity, based on data for a plurality of the other entities. An output network is arranged to receive data output by the relational network, and use the received data to select a respective action.
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公开(公告)号：US20210081795A1
公开(公告)日：2021-03-18
申请号：US17107621
申请日：2020-11-30
Applicant: DeepMind Technologies Limited
Inventor： Mike Chrzanowski , Jack William Rae , Ryan Faulkner , Theophane Guillaume Weber , David Nunes Raposo , Adam Anthony Santoro
Abstract: A system including one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to implement a memory and memory-based neural network is described. The memory is configured to store a respective memory vector at each of a plurality of memory locations in the memory. The memory-based neural network is configured to: at each of a plurality of time steps: receive an input; determine an update to the memory, wherein determining the update comprising applying an attention mechanism over the memory vectors in the memory and the received input; update the memory using the determined update to the memory; and generate an output for the current time step using the updated memory.
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公开(公告)号：US10853725B2
公开(公告)日：2020-12-01
申请号：US16415954
申请日：2019-05-17
Applicant: DeepMind Technologies Limited
Inventor： Mike Chrzanowski , Jack William Rae , Ryan Faulkner , Theophane Guillaume Weber , David Nunes Raposo , Adam Anthony Santoro
Abstract: A system including one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to implement a memory and memory-based neural network is described. The memory is configured to store a respective memory vector at each of a plurality of memory locations in the memory. The memory-based neural network is configured to: at each of a plurality of time steps: receive an input; determine an update to the memory, wherein determining the update comprising applying an attention mechanism over the memory vectors in the memory and the received input; update the memory using the determined update to the memory; and generate an output for the current time step using the updated memory.
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