CAJ | 학술논문

RoboCup 是世界上规模最大的机器人足球大赛,包括软件仿真与硬件实体两类项目的比赛.RoboCup 仿真2D 作为软件仿真项目的重要组成部分,成为研究人工智能和多 Agent 智能体协作的优秀实验平台.将 Q 学习应用到 RoboCup仿真2D 比赛的前场进攻动作决策中,通过引入区域划分,基于区域划分的奖惩函数和对真人足球赛中动作决策的模拟,在经过大量周期的学习训练后,使 Agent能够进行自主动作决策,从而加强了多 Agent的前场进攻实力.
RoboCup 시세계상규모최대적궤기인족구대새,포괄연건방진여경건실체량류항목적비새.RoboCup 방진2D 작위연건방진항목적중요조성부분,성위연구인공지능화다 Agent 지능체협작적우수실험평태.장 Q 학습응용도 RoboCup방진2D 비새적전장진공동작결책중,통과인입구역화분,기우구역화분적장징함수화대진인족구새중동작결책적모의,재경과대량주기적학습훈련후,사 Agent능구진행자주동작결책,종이가강료다 Agent적전장진공실력.
@@@@RoboCup(Robot World Cup)is the largest scale robot soccer game, including software simulation and hardware enti-ties from two categories project competition. As an important part of software simulation project, RoboCup simulation 2D has become an outstanding experiment platform in which artificial intelligence and multi-agent cooperation are studied. This paper applies the Q-Learning to RoboCup simulation 2D match local attacking decision, through the introduction of zoning, incentive functions based zoning and decision making for real soccer game action simulation, after training a large number of cycles of learning, making the Agent do the independent action decision, thereby strengthening the multi-agent attacking strength.