Neuro-evolutionary Frameworks for Generalized Learning Agents

The ultimate aim of artificial intelligence research is to develop agents with truly intelligent behaviors, akin to those found in humans and animals. To this end, a number of tools and techniques have been developed. In recent years, two approaches in particular - deep learning (DL) and reinforcement learning (RL), seem to have made considerable progress towards this goal. Both these fields have been widely studied, with numerous successful examples [22, 29, 42, 25, 40] reported, particularly in recent years. However, even with the unprecedented success of recent approaches such as deep RL [28, 27, 36], poor sample efficiency and limited generalization remain major concerns to be addressed, keeping in view the ultimate goal of developing general purpose agents. The poor generalization capability of DL is exposed by its liability to deception when presented with adversarial examples [30, 39]. Recent work [38], showed that it was possible to hurt the performance of DLbased image recognition systems by carefully altering just a single pixel.