Interactive Learning in Noisy and Changing Environments

嘈杂和变化的环境中的互动学习

• 批准号: 9625557
• 负责人: Anthony Kuh
• 金额: $ 22.93万
• 依托单位: University of Hawaii
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-01 至 2000-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9625557&HistoricalAwards=false
• 关键词: Interactive; Learning; Noisy; Changing; Environments

9625557 Kuh This project will study learning models in noisy and changing environments. The environments are generally unknown and are learned through observation examples presented at discrete times. Previous research has modeled and analyzed how supervised learning algorithms (algorithms where inputs receive labels) behave when confronted with slowly changing environments. This research will continue to analyze the behavior of supervised learning algorithms, but will focus on more complex reinforcement learning algorithms. The primary objectives are to obtain a better understanding of reinforcement learning methods to problems in signal processing and communications. Two applications areas that are well suited to reinforcement learning approaches are sequential detection problems and networking problems. ***

9625557 Kuh这个项目将研究在嘈杂和不断变化的环境中的学习模型。 环境通常是未知的，并通过在离散时间呈现的观察示例来学习。以前的研究已经建模和分析了监督学习算法（输入接收标签的算法）在面对缓慢变化的环境时的行为。 这项研究将继续分析监督学习算法的行为，但将专注于更复杂的强化学习算法。 主要目标是更好地理解信号处理和通信中的强化学习方法。 两个非常适合强化学习方法的应用领域是顺序检测问题和网络问题。***