The Effect of Neuromodulations on the Adaptability of Evolved Neurocontrollers

摘要

One of the serious drawbacks in Evolutionary Robotics approaches is that evolved agents in simulated environments often show significantly different behavior in real environments due to unforeseen perturbations. This is sometimes referred to as the gap problem. In order to alleviate this problem, we have so far proposed Dynamically-Rearranging Neural Networks(DRNN) by introducing the concept of neuromodula-tions with a diffusion-reaction mechanism of signaling molecules to so-called neuromodulators. In this study, an analysis of the evolved DRNN and a quantitative comparison with standard neural networks are presented. Through this analysis, we discuss the effect of neuromodulation on the adaptability of the evolved neurocontrollers.