Distortion based potential game for distributed coverage control.

In this work, we propose a multi-agent learning framework to address the mobile sensor coverage problem in which the minimum mutual information between the agents (mobile sensors) and their environment defines the agent-strategy selection rule towards the system Nash equilibrium in a potential game setting. Initially, the agents infer the environment behavior by means of the Gaussian process regression (GPR) approach, using their own information and the information provided by their neighborhood. Then, the rate distortion function (RDF) is used to minimize the mutual information between each agent and its environment by means of the Blahut-Arimoto algorithm, from which, the resulting conditional probability and the parameter λ have the highest relevance, since, the former describes the similitude between the agent information and its environment, and the latter, due to its influence in the distortion and gathered environment information, defines the rationality measure in the learning process. Finally, the expected distortion defined in the RDF, allows the formulation of a distortion based potential function and the consequent equilibrium convergence. [ABSTRACT FROM AUTHOR]