Using augmenting modular neural networks to evolve neuro-controllers for a team of underwater vehicles

The paper presents a new generative neuro-evolutionary method called augmenting modular neural networks (AMNN). As the name of the method implies, its purpose is to construct neural networks with a modular architecture. In addition to the modularity itself, neural networks evolving according to AMNN are also characterized by gradually expanding architecture. In the beginning of the evolutionary process, all networks consist of only output modules (or a single module). After some time, if the architecture of all networks is insufficient to effectively perform a task, all of them are augmented by one hidden module. In the following generations, further hidden modules are also added and this procedure is continued until some stopping criterion is satisfied. To test performance of AMNN, the method was used to evolve neuro-controllers for a team of underwater vehicles whose common goal was to capture other vehicle behaving by a deterministic strategy (predator-prey problem). The experiments were carried out in simulation, whereas their results were used to compare AMNN with neuro-evolutionary methods designed for building monolithic neural networks.