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公开(公告)号：US11328183B2
公开(公告)日：2022-05-10
申请号：US17019919
申请日：2020-09-14
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Yujia Li , Razvan Pascanu , Peter William Battaglia , Theophane Guillaume Weber , Lars Buesing , David Paul Reichert , Arthur Clement Guez , Danilo Jimenez Rezende , Adrià Puigdomènech Badia , Oriol Vinyals , Nicolas Manfred Otto Heess , Sebastien Henri Andre Racaniere
Abstract: A neural network system is proposed. The neural network can be trained by model-based reinforcement learning to select actions to be performed by an agent interacting with an environment, to perform a task in an attempt to achieve a specified result. The system may comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.
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公开(公告)号：US20210089834A1
公开(公告)日：2021-03-25
申请号：US17114324
申请日：2020-12-07
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Yujia Li , Razvan Pascanu , Peter William Battaglia , Theophane Guillaume Weber , Lars Buesing , David Paul Reichert , Oriol Vinyals , Nicolas Manfred Otto Heess , Sebastien Henri Andre Racaniere
Abstract: A neural network system is proposed to select actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The system may include a controller to receive state data and context data, and to output action data. The system may also include an imagination module to receive the state and action data, and to output consequent state data. The system may also include a manager to receive the state data and the context data, and to output route data which defines whether the system is to execute an action or to imagine. The system may also include a memory to store the context data.
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公开(公告)号：US20190266475A1
公开(公告)日：2019-08-29
申请号：US16403352
申请日：2019-05-03
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Shakir Mohamed , Silvia Chiappa , Sebastien Henri Andre Racaniere
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for environment simulation. In one aspect, a system comprises a recurrent neural network configured to, at each of a plurality of time steps, receive a preceding action for a preceding time step, update a preceding initial hidden state of the recurrent neural network from the preceding time step using the preceding action, update a preceding cell state of the recurrent neural network from the preceding time step using at least the initial hidden state for the time step, and determine a final hidden state for the time step using the cell state for the time step. The system further comprises a decoder neural network configured to receive the final hidden state for the time step and process the final hidden state to generate a predicted observation characterizing a predicted state of the environment at the time step.
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公开(公告)号：US11200482B2
公开(公告)日：2021-12-14
申请号：US16893565
申请日：2020-06-05
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Shakir Mohamed , Silvia Chiappa , Sebastien Henri Andre Racaniere
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for environment simulation. In one aspect, a system comprises a recurrent neural network configured to, at each of a plurality of time steps, receive a preceding action for a preceding time step, update a preceding initial hidden state of the recurrent neural network from the preceding time step using the preceding action, update a preceding cell state of the recurrent neural network from the preceding time step using at least the initial hidden state for the time step, and determine a final hidden state for the time step using the cell state for the time step. The system further comprises a decoder neural network configured to receive the final hidden state for the time step and process the final hidden state to generate a predicted observation characterizing a predicted state of the environment at the time step.
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公开(公告)号：US10713559B2
公开(公告)日：2020-07-14
申请号：US16403352
申请日：2019-05-03
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Shakir Mohamed , Silvia Chiappa , Sebastien Henri Andre Racaniere
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for environment simulation. In one aspect, a system comprises a recurrent neural network configured to, at each of a plurality of time steps, receive a preceding action for a preceding time step, update a preceding initial hidden state of the recurrent neural network from the preceding time step using the preceding action, update a preceding cell state of the recurrent neural network from the preceding time step using at least the initial hidden state for the time step, and determine a final hidden state for the time step using the cell state for the time step. The system further comprises a decoder neural network configured to receive the final hidden state for the time step and process the final hidden state to generate a predicted observation characterizing a predicted state of the environment at the time step.
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Patent Agency Ranking
公开(公告)号：US20200082227A1
公开(公告)日：2020-03-12
申请号：US16689017
申请日：2019-11-19
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Yujia Li , Razvan Pascanu , Peter William Battaglia , Theophane Guillaume Weber , Lars Buesing , David Paul Reichert , Oriol Vinyals , Nicolas Manfred Otto Heess , Sebastien Henri Andre Racaniere
Abstract: A neural network system is proposed to select actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The system may include a controller to receive state data and context data, and to output action data. The system may also include an imagination module to receive the state and action data, and to output consequent state data. The system may also include a manager to receive the state data and the context data, and to output route data which defines whether the system is to execute an action or to imagine. The system may also include a memory to store the context data.
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公开(公告)号：US20210073594A1
公开(公告)日：2021-03-11
申请号：US17019919
申请日：2020-09-14
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Yujia Li , Razvan Pascanu , Peter William Battaglia , Theophane Guillaume Weber , Lars Buesing , David Paul Reichert , Arthur Clement Guez , Danilo Jimenez Rezende , Adrià Puigdomènech Badia , Oriol Vinyals , Nicolas Manfred Otto Heess , Sebastien Henri Andre Racaniere
Abstract: A neural network system is proposed. The neural network can be trained by model-based reinforcement learning to select actions to be performed by an agent interacting with an environment, to perform a task in an attempt to achieve a specified result. The system may comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.
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公开(公告)号：US10860895B2
公开(公告)日：2020-12-08
申请号：US16689017
申请日：2019-11-19
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Yujia Li , Razvan Pascanu , Peter William Battaglia , Theophane Guillaume Weber , Lars Buesing , David Paul Reichert , Oriol Vinyals , Nicolas Manfred Otto Heess , Sebastien Henri Andre Racaniere
Abstract: A neural network system is proposed to select actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The system may include a controller to receive state data and context data, and to output action data. The system may also include an imagination module to receive the state and action data, and to output consequent state data. The system may also include a manager to receive the state data and the context data, and to output route data which defines whether the system is to execute an action or to imagine. The system may also include a memory to store the context data.
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公开(公告)号：US20200342289A1
公开(公告)日：2020-10-29
申请号：US16893565
申请日：2020-06-05
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Shakir Mohamed , Silvia Chiappa , Sebastien Henri Andre Racaniere
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for environment simulation. In one aspect, a system comprises a recurrent neural network configured to, at each of a plurality of time steps, receive a preceding action for a preceding time step, update a preceding initial hidden state of the recurrent neural network from the preceding time step using the preceding action, update a preceding cell state of the recurrent neural network from the preceding time step using at least the initial hidden state for the time step, and determine a final hidden state for the time step using the cell state for the time step. The system further comprises a decoder neural network configured to receive the final hidden state for the time step and process the final hidden state to generate a predicted observation characterizing a predicted state of the environment at the time step.
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公开(公告)号：US10776670B2
公开(公告)日：2020-09-15
申请号：US16689058
申请日：2019-11-19
Applicant: DeepMind Technologies Limited
Inventor： Daniel Pieter Wierstra , Yujia Li , Razvan Pascanu , Peter William Battaglia , Theophane Guillaume Weber , Lars Buesing , David Paul Reichert , Arthur Clement Guez , Danilo Jimenez Rezende , Adrià Puigdomènech Badia , Oriol Vinyals , Nicolas Manfred Otto Heess , Sebastien Henri Andre Racaniere
Abstract: A neural network system is proposed. The neural network can be trained by model-based reinforcement learning to select actions to be performed by an agent interacting with an environment, to perform a task in an attempt to achieve a specified result. The system may comprise at least one imagination core which receives a current observation characterizing a current state of the environment, and optionally historical observations, and which includes a model of the environment. The imagination core may be configured to output trajectory data in response to the current observation, and/or historical observations. The trajectory data comprising a sequence of future features of the environment imagined by the imagination core. The system may also include a rollout encoder to encode the features, and an output stage to receive data derived from the rollout embedding and to output action policy data for identifying an action based on the current observation.
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