A reinforcement learning based approach to play calling in football

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2021-03-11 DOI:10.1515/jqas-2021-0029

Preston Biro, S. Walker

引用次数: 2

Abstract

Abstract With the vast amount of data collected on football and the growth of computing power, many games involving decision choices can be optimized. The underlying rule is the maximization of an expected utility of outcomes and the law of large numbers. The data available allows one to compute with high accuracy the probabilities of outcomes of actions, and the well defined points system in the game allows for a specification of the terminal utilities. With some well established decision theory we can optimize choices for each single play level. A full exposition of the theory and analysis is presented in the paper.

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一种基于强化学习的足球比赛呼叫方法

随着大量足球数据的收集和计算能力的提高，许多涉及决策选择的比赛都可以进行优化。基本规则是预期效用的最大化和大数法则。可用的数据让玩家能够准确地计算出行动结果的概率，而游戏中定义良好的积分系统也让玩家能够明确终端效用。有了一些完善的决策理论，我们就可以优化每个游戏关卡的选择。本文对其理论和分析进行了全面的阐述。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.