Patents by Inventor Volodymyr Mnih
Volodymyr Mnih has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240160901Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network system used to control an agent interacting with an environment. One of the methods includes receiving a current observation; processing the current observation using a proposal neural network to generate a proposal output that defines a proposal probability distribution over a set of possible actions that can be performed by the agent to interact with the environment; sampling (i) one or more actions from the set of possible actions in accordance with the proposal probability distribution and (ii) one or more actions randomly from the set of possible actions; processing the current observation and each sampled action using a Q neural network to generate a Q value; and selecting an action using the Q values generated by the Q neural network.Type: ApplicationFiled: January 8, 2024Publication date: May 16, 2024Inventors: Tom Van de Wiele, Volodymyr Mnih, Andriy Mnih, David Constantine Patrick Warde-Farley
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Patent number: 11977983Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.Type: GrantFiled: September 14, 2020Date of Patent: May 7, 2024Assignee: DeepMind Technologies LimitedInventors: Mohammad Gheshlaghi Azar, Meire Fortunato, Bilal Piot, Olivier Claude Pietquin, Jacob Lee Menick, Volodymyr Mnih, Charles Blundell, Remi Munos
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Publication number: 20240144015Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The method includes: training an action selection policy neural network, and during the training of the action selection neural network, training one or more auxiliary control neural networks and a reward prediction neural network. Each of the auxiliary control neural networks is configured to receive a respective intermediate output generated by the action selection policy neural network and generate a policy output for a corresponding auxiliary control task. The reward prediction neural network is configured to receive one or more intermediate outputs generated by the action selection policy neural network and generate a corresponding predicted reward.Type: ApplicationFiled: November 3, 2023Publication date: May 2, 2024Inventors: Volodymyr Mnih, Wojciech Czarnecki, Maxwell Elliot Jaderberg, Tom Schaul, David Silver, Koray Kavukcuoglu
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Publication number: 20240127060Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.Type: ApplicationFiled: October 16, 2023Publication date: April 18, 2024Inventors: Hubert Josef Soyer, Lasse Espeholt, Karen Simonyan, Yotam Doron, Vlad Firoiu, Volodymyr Mnih, Koray Kavukcuoglu, Remi Munos, Thomas Ward, Timothy James Alexander Harley, Iain Robert Dunning
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Publication number: 20240104379Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an agent can be controlled using an action selection neural network that performs in-context reinforcement learning when controlling an agent on a new task.Type: ApplicationFiled: September 28, 2023Publication date: March 28, 2024Inventors: Michael Laskin, Volodymyr Mnih, Luyu Wang, Satinder Singh Baveja
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Publication number: 20240104389Abstract: In one aspect there is provided a method for training a neural network system by reinforcement learning. The neural network system may be configured to receive an input observation characterizing a state of an environment interacted with by an agent and to select and output an action in accordance with a policy aiming to satisfy an objective. The method may comprise obtaining a policy set comprising one or more policies for satisfying the objective and determining a new policy based on the one or more policies. The determining may include one or more optimization steps that aim to maximize a diversity of the new policy relative to the policy set under the condition that the new policy satisfies a minimum performance criterion based on an expected return that would be obtained by following the new policy.Type: ApplicationFiled: February 4, 2022Publication date: March 28, 2024Inventors: Tom Ben Zion Zahavy, Brendan Timothy O'Donoghue, Andre da Motta Salles Barreto, Johan Sebastian Flennerhag, Volodymyr Mnih, Satinder Singh Baveja
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Patent number: 11941088Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using recurrent attention. One of the methods includes determining a location in the first image; extracting a glimpse from the first image using the location; generating a glimpse representation of the extracted glimpse; processing the glimpse representation using a recurrent neural network to update a current internal state of the recurrent neural network to generate a new internal state; processing the new internal state to select a location in a next image in the image sequence after the first image; and processing the new internal state to select an action from a predetermined set of possible actions.Type: GrantFiled: May 5, 2022Date of Patent: March 26, 2024Assignee: DeepMind Technologies LimitedInventors: Volodymyr Mnih, Koray Kavukcuoglu
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Patent number: 11868894Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.Type: GrantFiled: January 4, 2023Date of Patent: January 9, 2024Assignee: DeepMind Technologies LimitedInventors: Hubert Josef Soyer, Lasse Espeholt, Karen Simonyan, Yotam Doron, Vlad Firoiu, Volodymyr Mnih, Koray Kavukcuoglu, Remi Munos, Thomas Ward, Timothy James Alexander Harley, Iain Robert Dunning
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Patent number: 11868866Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network system used to control an agent interacting with an environment. One of the methods includes receiving a current observation; processing the current observation using a proposal neural network to generate a proposal output that defines a proposal probability distribution over a set of possible actions that can be performed by the agent to interact with the environment; sampling (i) one or more actions from the set of possible actions in accordance with the proposal probability distribution and (ii) one or more actions randomly from the set of possible actions; processing the current observation and each sampled action using a Q neural network to generate a Q value; and selecting an action using the Q values generated by the Q neural network.Type: GrantFiled: November 18, 2019Date of Patent: January 9, 2024Assignee: Deep Mind Technologies LimitedInventors: Tom Van de Wiele, Volodymyr Mnih, Andriy Mnih, David Constantine Patrick Warde-Farley
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Patent number: 11842281Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The method includes: training an action selection policy neural network, and during the training of the action selection neural network, training one or more auxiliary control neural networks and a reward prediction neural network. Each of the auxiliary control neural networks is configured to receive a respective intermediate output generated by the action selection policy neural network and generate a policy output for a corresponding auxiliary control task. The reward prediction neural network is configured to receive one or more intermediate outputs generated by the action selection policy neural network and generate a corresponding predicted reward.Type: GrantFiled: February 24, 2021Date of Patent: December 12, 2023Assignee: DeepMind Technologies LimitedInventors: Volodymyr Mnih, Wojciech Czarnecki, Maxwell Elliot Jaderberg, Tom Schaul, David Silver, Koray Kavukcuoglu
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Publication number: 20230325635Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a policy neural network for use in controlling an agent using relative variational intrinsic control. In one aspect, a method includes: selecting a skill from a set of skills; generating a trajectory by controlling the agent using the policy neural network while the policy neural network is conditioned on the selected skill; processing an initial observation and a last observation using a relative discriminator neural network to generate a relative score; processing the last observation using an absolute discriminator neural network to generate an absolute score; generating a reward for the trajectory from the absolute score corresponding to the selected skill and the relative score corresponding to the selected skill; and training the policy neural network on the reward for the trajectory.Type: ApplicationFiled: September 10, 2021Publication date: October 12, 2023Inventors: David Constantine Patrick Warde-Farley, Steven Stenberg Hansen, Volodymyr Mnih, Kate Alexandra Baumli
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Patent number: 11783182Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.Type: GrantFiled: February 8, 2021Date of Patent: October 10, 2023Assignee: DeepMind Technologies LimitedInventors: Volodymyr Mnih, Adrià Puigdomènech Badia, Alexander Benjamin Graves, Timothy James Alexander Harley, David Silver, Koray Kavukcuoglu
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Patent number: 11727281Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent that interacts with an environment. In one aspect, a system comprises: an action selection subsystem that selects actions to be performed by the agent using an action selection policy generated using an action selection neural network; a reward subsystem that is configured to: receive an observation characterizing a current state of the environment and an observation characterizing a goal state of the environment; generate a reward using an embedded representation of the observation characterizing the current state of the environment and an embedded representation of the observation characterizing the goal state of the environment; and a training subsystem that is configured to train the action selection neural network based on the rewards generated by the reward subsystem using reinforcement learning techniques.Type: GrantFiled: January 27, 2022Date of Patent: August 15, 2023Assignee: DeepMind Technologies LimitedInventors: David Constantine Patrick Warde-Farley, Volodymyr Mnih
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Publication number: 20230153617Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.Type: ApplicationFiled: January 4, 2023Publication date: May 18, 2023Inventors: Hubert Josef Soyer, Lasse Espeholt, Karen Simonyan, Yotam Doron, Vlad Firoiu, Volodymyr Mnih, Koray Kavukcuoglu, Remi Munos, Thomas Ward, Timothy James Alexander Harley, Iain Robert Dunning
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Patent number: 11593646Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection neural network used to select actions to be performed by an agent interacting with an environment. In one aspect, a system comprises a plurality of actor computing units and a plurality of learner computing units. The actor computing units generate experience tuple trajectories that are used by the learner computing units to update learner action selection neural network parameters using a reinforcement learning technique. The reinforcement learning technique may be an off-policy actor critic reinforcement learning technique.Type: GrantFiled: February 5, 2019Date of Patent: February 28, 2023Assignee: DeepMind Technologies LimitedInventors: Hubert Josef Soyer, Lasse Espeholt, Karen Simonyan, Yotam Doron, Vlad Firoiu, Volodymyr Mnih, Koray Kavukcuoglu, Remi Munos, Thomas Ward, Timothy James Alexander Harley, Iain Robert Dunning
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Publication number: 20220392206Abstract: A system that is configured to receive a sequence of task inputs and to perform a machine learning task is described. The system includes a reinforcement learning (RL) neural network and a task neural network. The RL neural network is configured to: generate, for each task input of the sequence of task inputs, a respective decision that determines whether to encode the task input or to skip the task input, and provide the respective decision of each task input to the task neural network.Type: ApplicationFiled: November 13, 2020Publication date: December 8, 2022Inventors: Viorica PATRAUCEAN, Bilal PIOT, Joao CARREIRA, Volodymyr MNIH, Simon OSINDERO
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Patent number: 11507827Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributed training of reinforcement learning systems. One of the methods includes receiving, by a learner, current values of the parameters of the Q network from a parameter server, wherein each learner maintains a respective learner Q network replica and a respective target Q network replica; updating, by the learner, the parameters of the learner Q network replica maintained by the learner using the current values; selecting, by the learner, an experience tuple from a respective replay memory; computing, by the learner, a gradient from the experience tuple using the learner Q network replica maintained by the learner and the target Q network replica maintained by the learner; and providing, by the learner, the computed gradient to the parameter server.Type: GrantFiled: October 14, 2019Date of Patent: November 22, 2022Assignee: DeepMind Technologies LimitedInventors: Praveen Deepak Srinivasan, Rory Fearon, Cagdas Alcicek, Arun Sarath Nair, Samuel Blackwell, Vedavyas Panneershelvam, Alessandro De Maria, Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Mustafa Suleyman
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Publication number: 20220261647Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.Type: ApplicationFiled: April 29, 2022Publication date: August 18, 2022Inventors: Volodymyr Mnih, Adrià Puigdomènech Badia, Alexander Benjamin Graves, Timothy James Alexander Harley, David Silver, Koray Kavukcuoglu
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Patent number: 11354548Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using recurrent attention. One of the methods includes determining a location in the first image; extracting a glimpse from the first image using the location; generating a glimpse representation of the extracted glimpse; processing the glimpse representation using a recurrent neural network to update a current internal state of the recurrent neural network to generate a new internal state; processing the new internal state to select a location in a next image in the image sequence after the first image; and processing the new internal state to select an action from a predetermined set of possible actions.Type: GrantFiled: July 13, 2020Date of Patent: June 7, 2022Assignee: DeepMind Technologies LimitedInventors: Volodymyr Mnih, Koray Kavukcuoglu
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Publication number: 20220164673Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent that interacts with an environment. In one aspect, a system comprises: an action selection subsystem that selects actions to be performed by the agent using an action selection policy generated using an action selection neural network; a reward subsystem that is configured to: receive an observation characterizing a current state of the environment and an observation characterizing a goal state of the environment; generate a reward using an embedded representation of the observation characterizing the current state of the environment and an embedded representation of the observation characterizing the goal state of the environment; and a training subsystem that is configured to train the action selection neural network based on the rewards generated by the reward subsystem using reinforcement learning techniques.Type: ApplicationFiled: January 27, 2022Publication date: May 26, 2022Inventors: David Constantine Patrick Warde-Farley, Volodymyr Mnih