Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function,

Abstract

Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This paper describes our work on a Memory-based technique for to choose an action based on a continuous-valued state attribute indicating the position of an opponent. We investigate the question of how an agent performs in nondeterministic variations of the training situations. Our experiments indicate that when the random variations fall within some bound of the initial training, the agent performs better with some initial training rather than from a tabula-rasa. 1 Introduction One of the ultimate goals subjacent to the development of intelligent agents is to have multiple agents collaborating in the achievement of tasks in the presence of hostile opponents. Our research works towards this broad goal from a Machine Learning perspective. We are particularly interested in investigating how an intelli...