Q learning is of great importance in reinforcement learning.
Q 学习是一种重要的强化学习算法。
参考来源 - 一种多步Q强化学习方法 in CThus,reinforcement learning methods have wide application areas in solving complex optimization and decision problems,where teacher signals are hard to be obtained.
因此增强学习在求解无法获得教师信号的复杂优化与决策问题中具有广泛的应用前景。
参考来源 - 增强学习及其在移动机器人导航与控制中的应用研究That presented in this paper is a new utility clustering based reinforcement learning algorithm called U-Clustering. Unlike the U-Tree,it does not use fringe and related statistical test at all.
提出了一个新的效用聚类激励学习算法U—Clustering。
参考来源 - 期刊学术社区3. Since traditional sparse coding does not consider the intra-class variations of the features, two new sparse coding algorithms based on reinforcement learning are proposed to increase the classification property of the features.
3.针对传统稀疏编码未对特征的类内距离进行有效约束这个缺点,提出了两种 基于再励学习的稀疏编码算法。
参考来源 - 人脸图像分析和识别方法研究The concepts of Markov decision process and reinforcement learning are introduced firstly.
论文首先介绍了马尔可夫决策过程的基本概念和再励学习的框架。
参考来源 - 再励学习在通信网络最优控制中的应用研究Computer game playing is an interesting subject in artificial intelligence research. Reinforcement learning is a machine learning method to study Superior strategy through trial-and-error search and delayed reward from Environment-feedback.
人机博弈是人工智能领域中的一个重要主题,激励学习是一种智能体通过不断地试错,从环境反馈中得到延迟奖惩信息,积累经验,最终学习到最优策略的机器学习方法。
参考来源 - 基于激励学习的中国象棋研究·2,447,543篇论文数据,部分数据来源于NoteExpress
以上来源于: WordNet
Learning is of great importance in reinforcement learning.
学习是一种重要的强化学习算法。
Without reinforcement learning is only short term and easily lost.
没有巩固的学习只能是短期的,很快遗忘的。
This sample graph is from a simple reinforcement learning application that USES Q learning.
这个示例图是从使用Q学习的一个简单增强式学习应用程序中得到的。
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