Coevolution of Multiagent Systems using NEAT

This experiment attempts to use NeuroEvolution of Augmenting Topologies (NEAT) create a Multiagent System that coevolves cooperative learning agents with a learning task in the form of three predators hunting the prey. Since the prey is hardcoded in a way that eludes the nearest predator, methods to capture the prey elude supervised learning techniques, i.e. cooperation is required to succeed. This is in some part an extension of Yong and Miikkulainen’s experiment Coevolution of Role-Based Cooperation in Multiagent Systems, except the Multiagent Systems use NEAT for the adaptive method rather than Symbiotic, Adaptive NeuroEvolution Enforced SubPopulations (SANE ESP). The NEAT multiagent system consisted of coevolving three heterogeneous networks with a common fitness function that rewards being close to the prey upon capture or the time limit imposed. The fitness scores among the population promisingly grew, but stagnation proved to be a large obstacle during later generations. Emergent behaviors of teamwork and clear evidence of heterogeneity leading to different roles were all found in the behavior and topology. This experiment made use of past research to create a rudimentary NEAT system that wouldn’t have been able to accomplish the same goal with a homogeneous or single agent system.