The added value of gating in evolved neurocontrollers

Abstract

While the concept of gating has been explored in past studies of neural networks, and neural network controllers have been successfully designed through evolutionary computation methods, very little past work has focused on empirically determining the value of adding gating to evolved neural network architectures. In this study, we do precisely that, by examining a neural architecture and genetic representation that explicitly permits the use of gating connections in a neurocontroller, and comparing the evolved controller performance to similar evolved controllers where gating connections are not explicitly included. The performance of these different approaches is evaluated in evolving a neurocontroller for an autonomous agent navigating through a simulated predator-prey environment. We find that the neural architecture that explicitly allows gating clearly outperforms three other architectures without gating, suggesting that there is a clear benefit to having gating connections directed by a command module. Further analysis of the best evolved agent reveals that its controller executes by producing command signals that encode high-level goals, which then modify low-level behaviors to achieve those goals, supporting the hypothesis that allowing gated connections in neural networks substantially improves the neurocontrollers that can be evolved.