Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating suggested communications during a multi-agent interaction using a language model neural network.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining principal components of a data set using multi-agent interactions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting an input vocabulary for a machine learning model using power indices. One of the methods includes computing a respective score for each of a plurality of text tokens in an initial vocabulary and then selecting the text tokens in the input vocabulary based on the respective scores.

Abstract: Machine learning techniques for multi-agent systems in which agents interact whilst performing their respective tasks. The techniques enable agents to learn to cooperate with one another, in particular by mixing incentives, in a way that improves their collective efficiency.

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.