Turing Learning is a method for the reverse engineering of agent behaviors. This approach was inspired by the Turing test where a machine can pass if its behaviour is indistinguishable from that of a human. Nash memory is a memory mechanism for coevolution. It guarantees monotonicity in convergence. This thesis explores the integration of such memory mechanism with Turing Learning for faster learning of agent behaviors. We employ the Enki robot simulation platform and learn the aggregation behavior of epuck robots. Our experiments indicate that using Nash memory can reduce the computation time by 35.4% and result in faster convergence for the aggregation game. This repository corresponds to the code and data for the thesis of the same title by Shuai Wang. If there is any question, please feel free to send an email to shuai.wang@vu.nl.