论文标题
AI足球的深Q网络
Deep Q-Network for AI Soccer
论文作者
论文摘要
强化学习在游戏的应用中表现出了出色的表现，尤其是在Atari游戏和GO中。基于这些成功的示例，我们试图将著名的强化学习算法（深Q-network）应用于AI足球游戏。 AI足球是5：5机器人足球比赛，每个参与者都会开发一种算法，该算法控制一个团队中的五个机器人以击败对手参与者。 Deep Q-Network旨在实现我们的原始奖励，状态空间和训练每个代理的行动空间，以便在游戏过程中可以在不同情况下采取适当的操作。我们的算法能够成功培训代理商，并且通过对10支希望参加AI足球国际比赛的10支球队的小型竞争，其表现得到了初步证明。比赛是由AI世界杯委员会组织的，并与WCG 2019 Xi'an AI大师组织。有了我们的算法，我们在这场国际比赛中与来自39个国家的130支球队的国际比赛中获得了16轮的成就。
Reinforcement learning has shown an outstanding performance in the applications of games, particularly in Atari games as well as Go. Based on these successful examples, we attempt to apply one of the well-known reinforcement learning algorithms, Deep Q-Network, to the AI Soccer game. AI Soccer is a 5:5 robot soccer game where each participant develops an algorithm that controls five robots in a team to defeat the opponent participant. Deep Q-Network is designed to implement our original rewards, the state space, and the action space to train each agent so that it can take proper actions in different situations during the game. Our algorithm was able to successfully train the agents, and its performance was preliminarily proven through the mini-competition against 10 teams wishing to take part in the AI Soccer international competition. The competition was organized by the AI World Cup committee, in conjunction with the WCG 2019 Xi'an AI Masters. With our algorithm, we got the achievement of advancing to the round of 16 in this international competition with 130 teams from 39 countries.
