  With artificial intelligence systems becoming ubiquitous in our society, its
designers will soon have to start to consider its social dimension, as many of
these systems will have to interact among them to work efficiently. With this
in mind, we propose a decentralized deep reinforcement learning algorithm for
the design of cooperative multi-agent systems. The algorithm is based on the
hypothesis that highly correlated actions are a feature of cooperative systems,
and hence, we propose the insertion of an auxiliary objective of maximization
of the mutual information between the actions of agents in the learning
problem. Our system is applied to a social dilemma, a problem whose optimal
solution requires that agents cooperate to maximize a macroscopic performance
function despite the divergent individual objectives of each agent. By
comparing the performance of the proposed system to a system without the
auxiliary objective, we conclude that the maximization of mutual information
among agents promotes the emergence of cooperation in social dilemmas.
