In this section, we analyse the behaviour of the neural controllers and the behavioural patterns of the agents with respect to the possibilities of their parameter space. The goal is to test if the learning rule proposed here is effective for driving the agent near critical points. As we observe below, the 10 agents display quite similar behaviour for each environment, despite the fact that each one has learned different values of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${c}_{ik}^{\ast }$$\end{document}cik∗ and Jij. We are interested in analysing if there is anything special about the configuration reached by the agents after learning.