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Patent Agency Ranking
公开(公告)号：US20220366245A1
公开(公告)日：2022-11-17
申请号：US17763901
申请日：2020-09-23
Applicant: DeepMind Technologies Limited
Inventor： Arthur Clement Guez , Fabio Viola , Theophane Guillaume Weber , Lars Buesing , Nicolas Manfred Otto Heess
IPC: G06N3/08
Abstract: A reinforcement learning method and system that selects actions to be performed by a reinforcement learning agent interacting with an environment. A causal model is implemented by a hindsight model neural network and trained using hindsight i.e. using future environment state trajectories. As the method and system does not have access to this future information when selecting an action, the hindsight model neural network is used to train a model neural network which is conditioned on data from current observations, which learns to predict an output of the hindsight model neural network.
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公开(公告)号：US10572776B2
公开(公告)日：2020-02-25
申请号：US16403343
申请日：2019-05-03
Applicant: DeepMind Technologies Limited
Inventor： Fabio Viola , Piotr Wojciech Mirowski , Andrea Banino , Razvan Pascanu , Hubert Josef Soyer , Andrew James Ballard , Sudarshan Kumaran , Raia Thais Hadsell , Laurent Sifre , Rostislav Goroshin , Koray Kavukcuoglu , Misha Man Ray Denil
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. In one aspect, a method of training an action selection policy neural network for use in selecting actions to be performed by an agent navigating through an environment to accomplish one or more goals comprises: receiving an observation image characterizing a current state of the environment; processing, using the action selection policy neural network, an input comprising the observation image to generate an action selection output; processing, using a geometry-prediction neural network, an intermediate output generated by the action selection policy neural network to predict a value of a feature of a geometry of the environment when in the current state; and backpropagating a gradient of a geometry-based auxiliary loss into the action selection policy neural network to determine a geometry-based auxiliary update for current values of the network parameters.
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公开(公告)号：US20200151515A1
公开(公告)日：2020-05-14
申请号：US16745757
申请日：2020-01-17
Applicant: DeepMind Technologies Limited
Inventor： Fabio Viola , Piotr Wojciech Mirowski , Andrea Banino , Razvan Pascanu , Hubert Josef Soyer , Andrew James Ballard , Sudarshan Kumaran , Raia Thais Hadsell , Laurent Sifre , Rostislav Goroshin , Koray Kavukcuoglu , Misha Man Ray Denil
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. In one aspect, a method of training an action selection policy neural network for use in selecting actions to be performed by an agent navigating through an environment to accomplish one or more goals comprises: receiving an observation image characterizing a current state of the environment; processing, using the action selection policy neural network, an input comprising the observation image to generate an action selection output; processing, using a geometry-prediction neural network, an intermediate output generated by the action selection policy neural network to predict a value of a feature of a geometry of the environment when in the current state; and backpropagating a gradient of a geometry-based auxiliary loss into the action selection policy neural network to determine a geometry-based auxiliary update for current values of the network parameters.
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公开(公告)号：US11074481B2
公开(公告)日：2021-07-27
申请号：US16745757
申请日：2020-01-17
Applicant: DeepMind Technologies Limited
Inventor： Fabio Viola , Piotr Wojciech Mirowski , Andrea Banino , Razvan Pascanu , Hubert Josef Soyer , Andrew James Ballard , Sudarshan Kumaran , Raia Thais Hadsell , Laurent Sifre , Rostislav Goroshin , Koray Kavukcuoglu , Misha Man Ray Denil
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. In one aspect, a method of training an action selection policy neural network for use in selecting actions to be performed by an agent navigating through an environment to accomplish one or more goals comprises: receiving an observation image characterizing a current state of the environment; processing, using the action selection policy neural network, an input comprising the observation image to generate an action selection output; processing, using a geometry-prediction neural network, an intermediate output generated by the action selection policy neural network to predict a value of a feature of a geometry of the environment when in the current state; and backpropagating a gradient of a geometry-based auxiliary loss into the action selection policy neural network to determine a geometry-based auxiliary update for current values of the network parameters.
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公开(公告)号：US20190266449A1
公开(公告)日：2019-08-29
申请号：US16403343
申请日：2019-05-03
Applicant: DeepMind Technologies Limited
Inventor： Fabio Viola , Piotr Wojciech Mirowski , Andrea Banino , Razvan Pascanu , Hubert Josef Soyer , Andrew James Ballard , Sudarshan Kumaran , Raia Thais Hadsell , Laurent Sifre , Rostislav Goroshin , Koray Kavukcuoglu , Misha Man Ray Denil
Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. In one aspect, a method of training an action selection policy neural network for use in selecting actions to be performed by an agent navigating through an environment to accomplish one or more goals comprises: receiving an observation image characterizing a current state of the environment; processing, using the action selection policy neural network, an input comprising the observation image to generate an action selection output; processing, using a geometry-prediction neural network, an intermediate output generated by the action selection policy neural network to predict a value of a feature of a geometry of the environment when in the current state; and backpropagating a gradient of a geometry-based auxiliary loss into the action selection policy neural network to determine a geometry-based auxiliary update for current values of the network parameters.
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