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 description data describing a computer programming task; receiving a first set of inputs for the 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; for each candidate computer program in a subset of the candidate computer programs and for each input in the first set: executing the candidate computer program on the input to generate an output; and selecting, from the candidate computer programs, one or more computer programs as synthesized computer programs for performing the computer programming task based at least in part on the outputs generated by executing the candidate computer programs in the subset on the inputs in the first set of inputs.

Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media, for training a policy neural network by repeatedly updating the policy neural network at each of a plurality of training iterations. One of the methods includes generating training data for the training iteration by controlling the agent in accordance with an improved policy that selects actions in response to input state representations. A best response computation is performed using (i) a candidate policy generated from respective policy neural networks as of one or more preceding iterations and (ii) a candidate value neural network. The candidate value neural network is configured to generate a value output that is an estimate of a value of the environment being in the state characterized by a state representation to complete a particular task. The policy neural network is updated by training the policy neural network on the training data.

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 description data describing a computer programming task; receiving a first set of inputs for the 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; for each candidate computer program in a subset of the candidate computer programs and for each input in the first set: executing the candidate computer program on the input to generate an output; and selecting, from the candidate computer programs, one or more computer programs as synthesized computer programs for performing the computer programming task based at least in part on the outputs generated by executing the candidate computer programs in the subset on the inputs in the first set of inputs.

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 description data describing a computer programming task; receiving a first set of inputs for the 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; for each candidate computer program in a subset of the candidate computer programs and for each input in the first set: executing the candidate computer program on the input to generate an output; and selecting, from the candidate computer programs, one or more computer programs as synthesized computer programs for performing the computer programming task based at least in part on the outputs generated by executing the candidate computer programs in the subset on the inputs in the first set of inputs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting an optimal feature point in a continuous domain for a group of agents. A computer-implemented system obtains, for each of a plurality of agents, respective training data that comprises a respective utility score for each of a plurality of discrete points in the continuous domain. The system trains, for each of the plurality of agents and on the respective training data for the agents, a respective neural network that is configured to receive an input comprising a point in the continuous domain and to generate as output a predicted utility score for the agent at the point.

Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media, for training a policy neural network by repeatedly updating the policy neural network at each of a plurality of training iterations. One of the methods includes generating training data for the training iteration by controlling the agent in accordance with an improved policy that selects actions in response to input state representations. A best response computation is performed using (i) a candidate policy generated from respective policy neural networks as of one or more preceding iterations and (ii) a candidate value neural network. The candidate value neural network is configured to generate a value output that is an estimate of a value of the environment being in the state characterized by a state representation to complete a particular task. The policy neural network is updated by training the policy neural network on the training data.