Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating computer code using neural networks. One of the methods includes receiving data describing a computer programming task; generating a plurality of candidate computer programs by sampling a plurality of output sequences from a set of one or more generative neural networks; clustering the plurality of candidate computer programs; for each cluster in a set of the clusters: processing each of the respective plurality of candidate computer programs in the cluster using a correctness estimation neural network to generate a correctness score for the candidate computer program; and selecting a representative computer program for the cluster using the correctness scores for the respective plurality of candidate computer programs in the cluster; and selecting one or more of the representative computer programs for the clusters as synthesized computer programs for performing the computer programming task.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an environment representation neural network of a reinforcement learning system controls an agent to perform a given task. In one aspect, the method includes: receiving a current observation input and a future observation input; generating, from the future observation input, a future latent representation of the future state of the environment; processing, using the environment representation neural network, to generate a current internal representation of the current state of the environment; generating, from the current internal representation, a predicted future latent representation; evaluating an objective function measuring a difference between the future latent representation and the predicted future latent representation; and determining, based on a determined gradient of the objective function, an update to the current values of the environment representation parameters.

Abstract: An iterative method is proposed to train an action selection system of a reinforcement learning system, based on a reward function which defines a reward value for each action. The reward value includes an intrinsic reward term generated based on the outputs of two encoder models: an online encoder model and a target encoder model. The online encoder model is iteratively trained based on a loss function, and the target encoder model is updated to bring it closer to the online encoder model.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating computer code using neural networks. One of the methods includes receiving data describing a computer programming task; generating a plurality of candidate computer programs by sampling a plurality of output sequences from a set of one or more generative neural networks; clustering the plurality of candidate computer programs; for each cluster in a set of the clusters: processing each of the respective plurality of candidate computer programs in the cluster using a correctness estimation neural network to generate a correctness score for the candidate computer program; and selecting a representative computer program for the cluster using the correctness scores for the respective plurality of candidate computer programs in the cluster; and selecting one or more of the representative computer programs for the clusters as synthesized computer programs for performing the computer programming task.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network used to select actions to be performed by an agent interacting with an environment. Implementations of the method model unpredictable aspects of the future, using hindsight. They use this information to disentangle inherently unpredictable, aleatoric variation, from epistemic uncertainty that arises from lack of knowledge of the environment. They then use the epistemic uncertainty, which relates to in principle predictable aspects of the environment, as a source of intrinsic reward to drive curiosity, i.e. exploration of the environment by the agent.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output sequence of discrete tokens using a diffusion model. In one aspect, a method includes generating, by using the diffusion model, a final latent representation of the sequence of discrete tokens that includes a determined value for each of a plurality of latent variables; applying a de-embedding matrix to the final latent representation of the output sequence of discrete tokens to generate a de-embedded final latent representation that includes, for each of the plurality of latent variables, a respective numeric score for each discrete token in a vocabulary of multiple discrete tokens; selecting, for each of the plurality of latent variables, a discrete token from among the multiple discrete tokens in the vocabulary that has a highest numeric score; and generating the output sequence of discrete tokens that includes the selected discrete tokens.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an environment representation neural network of a reinforcement learning system controls an agent to perform a given task. In one aspect, the method includes: receiving a current observation input and a future observation input; generating, from the future observation input, a future latent representation of the future state of the environment; processing, using the environment representation neural network, to generate a current internal representation of the current state of the environment; generating, from the current internal representation, a predicted future latent representation; evaluating an objective function measuring a difference between the future latent representation and the predicted future latent representation; and determining, based on a determined gradient of the objective function, an update to the current values of the environment representation parameters.

Abstract: A computer-implemented method of training a neural network. The method comprises processing a first transformed view of a training data item, e.g. an image, with a target neural network to generate a target output, processing a second transformed view of the training data item, e.g. image, with an online neural network to generate a prediction of the target output, updating parameters of the online neural network to minimize an error between the prediction of the target output and the target output, and updating parameters of the target neural network based on the parameters of the online neural network. The method can effectively train an encoder neural network without using labelled training data items, and without using a contrastive loss, i.e. without needing “negative examples” which comprise transformed views of different data items.