Training multi-agent teams from zero knowledge with the competitive coevolutionary team-based particle swarm optimiser

Abstract

A new competitive coevolutionary team-based particle swarm optimiser (CCPSO(t)) algorithm is developed to train multi-agent teams from zero knowledge. The CCPSO(t) algorithm is applied to train a team of agents to play simple soccer. The algorithm uses the charged particle swarm optimiser in a competitive and cooperative coevolutionary training environment to train neural network controllers for the players. The CCPSO(t) algorithm makes use of the FIFA league ranking relative fitness function to gather detailed performance metrics from each game played. The training performance and convergence behaviour of the particle swarm is analysed. A hypothesis is presented that explains the lack of convergence in the particle swarms. After applying a clustering algorithm on the particle positions, a detailed visual and quantitative analysis of the player strategies is presented. The final results show that the CCPSO(t) algorithm is capable of evolving complex gameplay strategies for a complex non-deterministic game.