Deepmind Technologies Limited

Google Inc.

Gdm Holding LLC

Timothy Paul Lillicrap

Nicolas Manfred Otto Heess

Gregory Duncan Wayne

Jack William Rae

Yuval Tassa

Sergey Levine

Adam Anthony Santoro

Chia-Chun Hung

Shixiang Gu

Mel Vecerik

Gabriel Barth-Maron

Martin Riedmiller

Thomas Lampe

Roland Hafner

Jonathan James Hunt

Ethan Holly

Joshua Simon Abramson

Ivaylo Popov

David Silver

Ivo Danihelka

Daniel Pieter Wierstra

Alexander Pritzel

Anna Potapenko

Tobias Markus Pohlen

Peter Conway Humphreys

Arun Ahuja

Sergey Bartunov

Tom Erez

Ilya Sutskever

Alexander Benjamin Graves

Timothy James Alexander Harley

Simon Osindero

Edward Thomas Grefenstette

Mehdi Mirza Mohammadi

Federico Javier Carnevale

Daniel Pieter Wiestra

Petko Ivanov Georgiev

Rui Zhu

Chen Yan

Jessica Paige Landon

Alistair Michael Muldal

Tamara Louise Von Glehn

David Antony Amos

Mihaela Rosca

Yan Wu

Fu-min Wang

Sergey Vladimir Levine

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an interactive agent can be controlled based on multi-modal inputs that include both an observation image and a natural language text sequence.

Controlling interactive agents using multi-modal inputs

A computer-implemented method for controlling a particular computer to execute a task is described. The method includes receiving a control input comprising a visual input, the visual input including one or more screen frames of a computer display that represent at least a current state of the particular computer, processing the control input using a neural network to generate one or more control outputs that are used to control the particular computer to execute the task, in which the one or more control outputs include an action type output that specifies at least one of a pointing device action or a keyboard action to be performed to control the particular computer; determining one or more actions from the one or more control outputs; and executing the one or more actions to control the particular computer.

Learned computer control using pointing device and keyboard actions

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input that is a sequence to generate a network output. In one aspect, one of the methods includes, for each particular sequence of layer inputs: for each attention layer in the neural network: maintaining episodic memory data; maintaining compressed memory data; receiving a layer input to be processed by the attention layer; and applying an attention mechanism over (i) the compressed representation in the compressed memory data for the layer, (ii) the hidden states in the episodic memory data for the layer, and (iii) the respective hidden state at each of the plurality of input positions in the particular network input to generate a respective activation for each input position in the layer input.

Augmenting attention-based neural networks to selectively attend to past inputs

Methods, systems, and apparatus for selecting actions to be performed by an agent interacting with an environment. One system includes a high-level controller neural network, low-level controller network, and subsystem. The high-level controller neural network receives an input observation and processes the input observation to generate a high-level output defining a control signal for the low-level controller. The low-level controller neural network receives a designated component of an input observation and processes the designated component and an input control signal to generate a low-level output that defines an action to be performed by the agent in response to the input observation. The subsystem receives a current observation characterizing a current state of the environment, determines whether criteria are satisfied for generating a new control signal, and based on the determination, provides appropriate inputs to the high-level and low-level controllers for selecting an action to be performed by the agent.

Selecting reinforcement learning actions using a low-level controller

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-efficient reinforcement learning. One of the systems is a system for training an actor neural network used to select actions to be performed by an agent that interacts with an environment by receiving observations characterizing states of the environment and, in response to each observation, performing an action selected from a continuous space of possible actions, wherein the actor neural network maps observations to next actions in accordance with values of parameters of the actor neural network, and wherein the system comprises: a plurality of workers, wherein each worker is configured to operate independently of each other worker, wherein each worker is associated with a respective agent replica that interacts with a respective replica of the environment during the training of the actor neural network.

Data-efficient reinforcement learning for continuous control tasks

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing associative memory. In one aspect a system comprises an associative memory neural network to process an input to generate an output that defines an energy corresponding to the input. A reading subsystem retrieves stored information from the associative memory neural network. The reading subsystem performs operations including receiving a given, i.e. query, input and retrieving a data element from the associative memory neural network that is associated with the given input. The retrieving is performed by iteratively adjusting the given input using the associative memory neural network.

Energy-based associative memory neural networks

Implementations utilize deep reinforcement learning to train a policy neural network that parameterizes a policy for determining a robotic action based on a current state. Some of those implementations collect experience data from multiple robots that operate simultaneously. Each robot generates instances of experience data during iterative performance of episodes that are each explorations of performing a task, and that are each guided based on the policy network and the current policy parameters for the policy network during the episode. The collected experience data is generated during the episodes and is used to train the policy network by iteratively updating policy parameters of the policy network based on a batch of collected experience data. Further, prior to performance of each of a plurality of episodes performed by the robots, the current updated policy parameters can be provided (or retrieved) for utilization in performance of the episode.

Deep reinforcement learning for robotic manipulation

A computer-implemented method for controlling a particular computer to execute a task is described. The method includes receiving a control input comprising a visual input, the visual input including one or more screen frames of a computer display that represent at least a current state of the particular computer; processing the control input using a neural network to generate one or more control outputs that are used to control the particular computer to execute the task, in which the one or more control outputs include an action type output that specifies at least one of a pointing device action or a keyboard action to be performed to control the particular computer; determining one or more actions from the one or more control outputs; and executing the one or more actions to control the particular computer.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a memory-based prediction system configured to receive an input observation characterizing a state of an environment interacted with by an agent and to process the input observation and data read from a memory to update data stored in the memory and to generate a latent representation of the state of the environment. The method comprises: for each of a plurality of time steps: processing an observation for the time step and data read from the memory to: (i) update the data stored in the memory, and (ii) generate a latent representation of the current state of the environment as of the time step; and generating a predicted return that will be received by the agent as a result of interactions with the environment after the observation for the time step is received.

Training an unsupervised memory-based prediction system to learn compressed representations of an environment

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for compressed sensing using neural networks. One of the methods includes receiving an input measurement of an input data item; for each of one or more optimization steps: processing a latent representation using a generator neural network to generate a candidate reconstructed data item, processing the candidate reconstructed data item using a measurement neural network to generate a measurement of the candidate reconstructed data item, and updating the latent representation to reduce an error between the measurement and the input measurement; and processing the latent representation after the one or more optimization steps using the generator neural network to generate a reconstruction of the input data item.

Compressed sensing using neural networks

A system for compressed data storage using a neural network. The system comprises a memory comprising a plurality of memory locations configured to store data; a query neural network configured to process a representation of an input data item to generate a query; an immutable key data store comprising key data for indexing the plurality of memory locations; an addressing system configured to process the key data and the query to generate a weighting associated with the plurality of memory locations; a memory read system configured to generate output memory data from the memory based upon the generated weighting associated with the plurality of memory locations and the data stored at the plurality of memory locations; and a memory write system configured to write received write data to the memory based upon the generated weighting associated with the plurality of memory locations.

Scalable and compressive neural network data storage system

Augmenting attentioned-based neural networks to selectively attend to past inputs

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.

Continuous control with deep reinforcement learning

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London, United Kingdom

Timothy Paul Lillicrap