Complexity in Coevolution and Deep Learning

Interactions between multiple competing and cooperating populations can drive the evolution of complexity. In this thesis, we will demonstrate that specific topologies of interactions are particularly successful at developing complexity, and will apply these lessons to a class of multi-agent deep learning algorithms, thereby demonstrating the parallels between the fields of artificial life and deep learning. Our analysis will cover multiple different evolutionary models, focusing on the paradigm of matrix games, which serve as an effective testbed for hypotheses about simulated evolution.