Q-learning using fuzzified states and weighted actions and its application to omni-direnctional mobile robot control
Q-learning using fuzzified states and weighted actions and its application to omni-direnctional mobile robot control
复制标题
使用模糊状态和加权动作的Q学习及其在全向移动机器人控制中的应用
DOI：
10.1109/cira.2009.5423227
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Jong
中科院分区：
文献类型：
--
作者：
Dong;In;Jong
传统的Q学习算法是由有限个离散化状态和离散化动作来描述的。当系统在连续的域中表示时，随着状态的快速变化，这可能会导致动作的突然转变。为了避免这种突然的行为转变，学习系统需要微调状态。然而，随着状态数的增加，学习时间显著增加，并且系统的计算代价变得很高。针对这一问题，本文提出了一种新的Q-学习算法，该算法使用模糊化状态和加权动作来更新其状态-动作值。通过将模糊集的概念应用于Q-学习的状态，并使用加权动作，该代理能够有效地响应状态的快速变化。将该算法应用于全方位移动机器人，实验结果验证了该方法的有效性。
The conventional Q-learning algorithm is described by a finite number of discretized states and discretized actions. When the system is represented in continuous domain, this may cause an abrupt transition of action as the state rapidly changes. To avoid this abrupt transition of action, the learning system requires fine-tuned states. However, the learning time significantly increases and the system becomes computationally expensive as the number of states increases. To solve this problem, this paper proposes a novel Q-learning algorithm, which uses fuzzified states and weighted actions to update its state-action value. By applying the concept of fuzzy set to the states of Q-learning and using the weighted actions, the agent efficiently responds to the rapid changes of the states. The proposed algorithm is applied to omni-directional mobile robot and the results demonstrate the effectiveness of the proposed approach.