Evolving a Non-playable Character team with Layered Learning

Abstract

Layered Learning is an iterative machine learning technique used to train agents how to perform tasks. The technique decomposes a task into simpler components and trains the agent to learn how to perform progressively more complex sub-tasks to solve the overall task. Layered Learning has been successfully used to instruct computer programs to solve Boolean-logic problems, teach robots how to walk, and train RoboCup soccer playing agents. The proposed work answers the question of how well does Layered Learning apply to the evolved development of a heterogeneous team of Non-playable Characters (NPCs) in a video game. The work compares the use of Layered Learning against evolving NPCs with monolithic based approaches. Experiment data show that Layered Learning can result in the successful development of NPCs and demonstrates that the approach performs well against monolithic evaluation.