Microsoft Technology Licensing, LLC

Deepmind Technologies Limited

Christopher Maddison

Ilya Sutskever

David Silver

Danilo Jimenez Rezende

Daniel Stefan Tarlow

Seyed Mohammadali Eslami

Thore Kurt Hartwig Graepel

Laurent Sifre

Arthur Clement Guez

Shih-Chieh Huang

Dan Rosenbaum

Marta Garnelo

Tiago Miguel Sargento Pires Ramalho

Methods, systems and apparatus, including computer programs encoded on computer storage media, for training a value neural network that is configured to receive an observation characterizing a state of an environment being interacted with by an agent and to process the observation in accordance with parameters of the value neural network to generate a value score. One of the systems performs operations that include training a supervised learning policy neural network; initializing initial values of parameters of a reinforcement learning policy neural network having a same architecture as the supervised learning policy network to the trained values of the parameters of the supervised learning policy neural network; training the reinforcement learning policy neural network on second training data; and training the value neural network to generate a value score for the state of the environment that represents a predicted long-term reward resulting from the environment being in the state.

Selecting actions to be performed by a reinforcement learning agent using tree search

Automated generation, or completion, or checking of source code is described whereby a probabilistic model having been trained using a corpus of natural source code examples is used. In various examples the probabilistic model comprises probability distributions describing belief about structure of natural source code and takes into account source code analysis from a compiler or other source code analyzer. In various examples, source code analysis may comprise syntactic structure, type information and other data about source code. In various examples, the trained probabilistic model is used to predict sequences of source code elements. For example, to generate source code, to auto-complete source code, to error check source code, to error correct source code or for other purposes.

Source code generation, completion, checking, correction

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Toronto, Canada

Christopher Maddison