An Introduction to Reinforcement Learning-猫眼课题宝

DOI：

10.1007/978-3-642-79629-6_5

发表时间：

1995

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

L. Kaelbling;M. Littman;A. Moore

本文从计算机科学家的角度考察了强化学习的历史基础和当前的一些工作。这是作者的一系列演讲的产物，包括北约高级研究所和AAAI ‘ 94和机器学习’ 94的教程。强化学习是一种流行的学习问题模型，它是通过与动态环境的试错交互来学习行为的代理所遇到的学习问题。它与心理学工作有很强的相似性，但在细节和“强化”一词的使用上有很大的不同。它应该被看作是一类问题，而不是一组技术。本文讨论了强化学习中的各种子问题，包括探索与利用、从延迟强化中学习、学习和使用模型、泛化和层次以及隐藏状态。最后对一些实际系统进行了调查，并对当前强化学习系统的实际效用进行了评估

This paper surveys the historical basis of reinforcement learning and some of the current work from a computer scientist’s point of view. It is an outgrowth of a number of talks given by the authors, including a NATO Advanced Study Institute and tutorials at AAAI’94 and Machine Learning’94. Reinforcement learning is a popular model of the learning problems that are encountered by an agent that learns behavior through trial-and-error interactions with a dynamic environment. It has a strong family resemblance to work in psychology, but differs considerably in the details and in the use of the word “reinforcement.” It is appropriately thought of as a class of problems, rather than as a set of techniques. The paper addresses a variety of subproblems in reinforcement learning, including exploration vs. exploitation, learning from delayed reinforcement, learning and using models, generalization and hierarchy, and hidden state. It concludes with a survey of some practical systems and an assessment of the practical utility of current reinforcement-learning systems

An Introduction to Reinforcement Learning

文献摘要

被引文献

相似文献