Imitation Learning in RoboCup Soccer by Neural Networks

Abstract

This paper proposes a framework for acquiring a low-level behavior of a soccer agent. The task of a learning agent is to mimic the behavior of a target agent with a well-trained behavior. Neural networks are used to represent the behavior of the target agent. In order to obtain a set of training data, we convert game logs of the target agent into a set of input-output pairs for the neural networks. We show the effectiveness of the proposed framework through computational experiments.