| 基于Q-Learning的智能体训练 |
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| 引用本文： | 赵增荣,韩提文.基于Q-Learning的智能体训练[J].石家庄铁道学院学报,2007,20(2):37-39,72. |
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| 作者姓名： | 赵增荣 韩提文 |
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| 作者单位： | [1]河北师范大学职业技术学院,河北石家庄050031 [2]河北工业职业技术学院成教部,河北石家庄050030 |
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| 基金项目： | 河北师范大学校科研和教改项目 |
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| 摘 要： | 针对机器人足球比赛的多智能体环境下智能体的训练问题,提出了一种将模糊控制与Q-Learning相结合的学习方法,并在学习过程中自动调节回报函数以获得最优策略,此方法的有效性在中型组的仿真平台上得到了验证,并取得了较好效果,还可将它改进应用于其他多智体环境。
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| 关 键 词： | 模糊控制 回报函数 |
| 文章编号： | 1006-3226（2007）02-0037-04 |
| 收稿时间： | 2006-11-06 |
| 修稿时间： | 2006-11-06 |
How to Train the Agent Based on the Way of Q-Learning |
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| Zhao Zengrong,Han Tiwen.How to Train the Agent Based on the Way of Q-Learning[J].Journal of Shijiazhuang Railway Institute,2007,20(2):37-39,72. |
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| Authors: | Zhao Zengrong Han Tiwen |
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| Institution: | 1. College of Vocation and Technology, Hebei Normal University, Shijiazhuang 050031, China; 2. The Adults Education Department of Hebei Industrial Vocational Technology College, Shijiazhuang 050030, China |
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| Abstract: | In order to solve the problem of agent training in the multi-agent circumstances of robot soccer, a new method of agent learning is put forward, which combines the fuzzy control with the Q-Learning. During the learning process, the reward function is controlled automatically to earn the optimal policy. It is proved that this method is effective on the simulation platform of middle-size league, and the simulation result is good. In addition, it could be adapted to apply in other multi-agent circumstances. |
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| Keywords: | Q-Learning |
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