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公开(公告)号:US11803750B2
公开(公告)日:2023-10-31
申请号:US17019927
申请日:2020-09-14
Applicant: DeepMind Technologies Limited
Inventor: Timothy Paul Lillicrap , Jonathan James Hunt , Alexander Pritzel , Nicolas Manfred Otto Heess , Tom Erez , Yuval Tassa , David Silver , Daniel Pieter Wierstra
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an actor neural network used to select actions to be performed by an agent interacting with an environment. One of the methods includes obtaining a minibatch of experience tuples; and updating current values of the parameters of the actor neural network, comprising: for each experience tuple in the minibatch: processing the training observation and the training action in the experience tuple using a critic neural network to determine a neural network output for the experience tuple, and determining a target neural network output for the experience tuple; updating current values of the parameters of the critic neural network using errors between the target neural network outputs and the neural network outputs; and updating the current values of the parameters of the actor neural network using the critic neural network.
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公开(公告)号:US20240177002A1
公开(公告)日:2024-05-30
申请号:US18497931
申请日:2023-10-30
Applicant: DeepMind Technologies Limited
Inventor: Timothy Paul Lillicrap , Jonathan James Hunt , Alexander Pritzel , Nicolas Manfred Otto Heess , Tom Erez , Yuval Tassa , David Silver , Daniel Pieter Wierstra
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an actor neural network used to select actions to be performed by an agent interacting with an environment. One of the methods includes obtaining a minibatch of experience tuples; and updating current values of the parameters of the actor neural network, comprising: for each experience tuple in the minibatch: processing the training observation and the training action in the experience tuple using a critic neural network to determine a neural network output for the experience tuple, and determining a target neural network output for the experience tuple; updating current values of the parameters of the critic neural network using errors between the target neural network outputs and the neural network outputs; and updating the current values of the parameters of the actor neural network using the critic neural network.
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公开(公告)号:US11151443B2
公开(公告)日:2021-10-19
申请号:US15424685
申请日:2017-02-03
Applicant: DeepMind Technologies Limited
Inventor: Ivo Danihelka , Gregory Duncan Wayne , Fu-min Wang , Edward Thomas Grefenstette , Jack William Rae , Alexander Benjamin Graves , Timothy Paul Lillicrap , Timothy James Alexander Harley , Jonathan James Hunt
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks with an external memory. One of the systems includes a sparse memory access subsystem that is configured to perform operations comprising generating a sparse set of reading weights that includes a respective reading weight for each of the plurality of locations in the external memory using the read key, reading data from the plurality of locations in the external memory in accordance with the sparse set of reading weights, generating a set of writing weights that includes a respective writing weight for each of the plurality of locations in the external memory, and writing the write vector to the plurality of locations in the external memory in accordance with the writing weights.
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公开(公告)号:US10776692B2
公开(公告)日:2020-09-15
申请号:US15217758
申请日:2016-07-22
Applicant: DeepMind Technologies Limited
Inventor: Timothy Paul Lillicrap , Jonathan James Hunt , Alexander Pritzel , Nicolas Manfred Otto Heess , Tom Erez , Yuval Tassa , David Silver , Daniel Pieter Wierstra
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training an actor neural network used to select actions to be performed by an agent interacting with an environment. One of the methods includes obtaining a minibatch of experience tuples; and updating current values of the parameters of the actor neural network, comprising: for each experience tuple in the minibatch: processing the training observation and the training action in the experience tuple using a critic neural network to determine a neural network output for the experience tuple, and determining a target neural network output for the experience tuple; updating current values of the parameters of the critic neural network using errors between the target neural network outputs and the neural network outputs; and updating the current values of the parameters of the actor neural network using the critic neural network.
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