Transfer Learning for Embodied Neuroevolution

Transfer Learning (TL) has been widely used in machine learning where the neuronal layers in a learned source Artificial Neural Network (ANN) are transferred to a target ANN so as to speed up the latter's learning. TL most often requires that the source and target domains are similar. However, its use in dissimilar domains as also in ANNs that use neuroevolution strategies has hardly been investigated. In this paper, we present a mechanism, suited for neuroevolution, that can identify specific neurons that need to be transferred. These Hot neurons from the source ANN, when transferred to the target ANN, helps in hastening the learning at the target. Simulations conducted using robots, clearly indicate that the mechanism is well suited for both similar and dissimilar tasks or environments.