2P1-F06 他者の状態価値の基づく協調・競合行動の獲得(ロボカップ・ロボットコンテスト)

概要

論文の詳細を見る
The existing reinforcement learning approaches have been suffering from the curse of dimension problem when they are applied to multiagent dynamic environments. One of the typical examples is a case of RoboCup competitions since other agents and their behaviors easily cause state and action space explosion. The keys for learning to acquire cooperative/competitive behaviors in such an environment are as follows:●a two-layer hierarchical system with multi learning modules is adopted to reduce the size of the sensor and action spaces. ●to what extent the other agent task has been achieved is estimated by observation and used as a state value in the top layer state space to accelerate the cooperative/competitive behavior learning. This paper presents a method of modular learning in a multiagent environment, by which the learning agent can acquire cooperative behaviors with its team mates and competitive ones against its opponents.
2007-05-11