Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network to perform an algorithmic task. According to one aspect, there is provided a method comprising: obtaining an input dataset; generating a first augmented dataset and a second augmented dataset, wherein for both the first augmented dataset and the second augmented dataset: applying the computational algorithm to the augmented dataset causes the same computational operations to be performed at a target computational step as would be performed by applying the computational algorithm to the input dataset; processing the first augmented dataset and the second augmented dataset using the neural network, comprising, for each augmented dataset: generating an intermediate representation of the augmented dataset at an intermediate layer of the neural network; and training the neural network on an objective function, wherein the objective function comprises a self-supervised loss term.

Abstract: A system for controlling an agent interacting with an environment to perform a task. The system includes an action selection neural network configured to generate action selection outputs that are used to select actions to be performed by the agent. The action selection neural network includes an encoder sub network configured to generate encoded representations of the current observations; an attention sub network configured to generate attention sub network outputs with the used of an attention mechanism; a recurrent sub network configured to generate recurrent sub network outputs; and an action selection sub network configured to generate the action selection outputs that are used to select the actions to be performed by the agent in response to the current observations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an embedding neural network based on score distributions. In one aspect, a system comprises: generating a first and second embedding of a data element, comprising: applying a first and second transformation to the data element to generate a respective first and second version of the data element and processing the respective versions using the embedding neural network to generate the respective first and second embeddings; generating, for the data element, a respective first and respective second score distribution, comprising: processing at least the first and the second embedding to generate the first and the second score distribution, respectively; and updating the current embedding network parameter values to optimize an objective function that is based on at least the first score distribution, that encourages a similarity between: (i) the first, and (ii) the second score distribution.