A Fuzzy Reinforcement Learning for a Ball Interception Problem
A Fuzzy Reinforcement Learning for a Ball Interception Problem
复制标题
球拦截问题的模糊强化学习
DOI：
10.1007/978-3-540-25940-4_52
复制
发表时间：
2003
期刊：
影响因子：
--
通讯作者：
H. Ishibuchi
中科院分区：
文献类型：
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作者：
T. Nakashima;Masayo Udo;H. Ishibuchi
在本文中，我们提出了一种称为模糊学习的强化学习方法，其中智能体根据基于模糊规则的系统的推理结果确定其行为。我们将所提出的方法应用于拦截另一个代理传递的球的足球代理。在提出的方法中，状态空间由学习代理保持的内部信息表示，例如球相对速度和相对位置。将状态空间划分为若干模糊子空间。该方法中的模糊if-then规则表示状态空间中的模糊子空间。模糊if-then规则的后续部分是一个运动向量，它表明了学习代理的移动方向和速度。如果球和智能体之间的距离变小，或者智能体追上了球，就会给学习智能体一个奖励。期望学习智能体最终获得有效的定位技能。
In this paper, we propose a reinforcement learning method called a fuzzyQ-learning where an agent determines its action based on the inference result by a fuzzy rule-based system. We apply the proposed method to a soccer agent that intercepts a passed ball by another agent. In the proposed method, the state space is represented by internal information the learning agent maintains such as the relative velocity and the relative position of the ball to the learning agent. We divide the state space into several fuzzy subspaces. A fuzzy if-then rule in the proposed method represents a fuzzy subspace in the state space. The consequent part of the fuzzy if-then rules is a motion vector that suggests the moving direction and velocity of the learning agent. A reward is given to the learning agent if the distance between the ball and the agent becomes smaller or if the agent catches up with the ball. It is expected that the learning agent finally obtains the efficient positioning skill.