Deepmind Technologies Limited

Google Inc.

Yusuf Aytar

Scott Ellison Reed

David Budden

Sergio Gomez Colmenarejo

Alexander Novikov

Joao Ferdinando Gomes De Freitas

Mel Vecerik

Jonathan Karl Scholz

David Barker

Serkan Cabi

Ksenia Konyushkova

Oriol Vinyals

Aaron Gerard Antonius Van Den Oord

Andrew Zisserman

Ziyu Wang

Misha Man Ray Denil

Rae Chan Jeong

Oleg O Sushkov

Konstantinos Bousmalis

Pierre Sermanet

Thomas Lampe

Jonathan Tompson

Tom Paine

Tobias Pfaff

Yuxiang Zhou

Debidatta Dwibedi

Jacqueline Ok-chan Kay

Francesco Nori

David Khosid

Techniques are disclosed that enable processing a video capturing a periodic activity using a repetition network to generate periodic output (e.g., a period length of the periodic activity captured in the video and/or a frame wise periodicity indication of the video capturing the periodic activity). Various implementations include a class agnostic repetition network which can be used to generate periodic output for a wide variety of periodic activities. Additional or alternative implementations include generating synthetic repetition videos which can be utilized to train the repetition network.

Class agnostic repetition counting in video(s) utilizing a temporal self-similarity matrix

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a policy neural network for use in controlling a real-world agent in a real-world environment. One of the methods includes training the policy neural network by optimizing a first task-specific objective that measures a performance of the policy neural network in controlling a simulated version of the real-world agent; and then training the policy neural network by jointly optimizing (i) a self-supervised objective that measures at least a performance of internal representations generated by the policy neural network on a self-supervised task performed on real-world data and (ii) a second task-specific objective that measures the performance of the policy neural network in controlling the simulated version of the real-world agent.

Domain adaptation for robotic control using self-supervised learning

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

Data-driven robot control

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network, wherein the action selection policy neural network is configured to process an observation characterizing a state of an environment to generate an action selection policy output, wherein the action selection policy output is used to select an action to be performed by an agent interacting with an environment. In one aspect, a method comprises: obtaining an observation characterizing a state of the environment subsequent to the agent performing a selected action; generating a latent representation of the observation; processing the latent representation of the observation using a discriminator neural network to generate an imitation score; determining a reward from the imitation score; and adjusting the current values of the action selection policy neural network parameters based on the reward using a reinforcement learning training technique.

Action selection neural network training using imitation learning in latent space

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London, United Kingdom

Yusuf Aytar