Deep Innovation Protection

Sebastian Risi, Kenneth O. Stanley

TL;DR: Deep Innovation Protection allows evolving complex world models end-to-end for 3D tasks.

Abstract: Evolutionary-based optimization approaches have recently shown promising results in domains such as Atari and robot locomotion but less so in solving 3D tasks directly from pixels. This paper presents a method called Deep Innovation Protection (DIP) that allows training complex world models end-to-end for such 3D environments. The main idea behind the approach is to employ multiobjective optimization to temporally reduce the selection pressure on specific components in a world model, allowing other components to adapt. We investigate the emergent representations of these evolved networks, which learn a model of the world without the need for a specific forward-prediction loss.

Keywords: Neuroevolution, innovation protection, world models, genetic algorithm