The topic of this book is Reinforcement Learning—which is a subfield of Machine Learning—focusing on the general and challenging problem of learning optimal behavior in complex environment. The learning process is driven only by reward value and observations obtained from the environment. This model is very general and can be applied to many practical situations from playing games to optimizing complex manufacture processes. Due to flexibility and generality, the field of Reinforcement Learning is developing very quickly and attracts lots of attention both from researchers trying to improve existing or create new methods, as well as from practitioners interested in solving their problems in the most efficient way. This book was written as an attempt to fill the obvious lack of practical and structured information about Reinforcement Learning methods and approaches. On one hand, there are lots of research activity all around the world, new research papers are being published almost every day, and a large portion of Deep Learning conferences such as NIPS or ICLR is dedicated to RL methods. There are several large research groups focusing on RL methods application in Robotics, Medicine, multi-agent systems, and others. The information about the recent research is widely available, but is too specialized and abstract to be understandable without serious efforts. Even worse is the situation with the practical aspect of RL application, as it is not always obvious how to make a step from the abstract method described in the mathematical-heavy form in a research paper to a working implementation solving actual problem. This makes it hard for somebody interested in the field to get an intuitive understanding of methods and ideas behind papers and conference talks. There are some very good blog posts about various RL aspects illustrated with working examples,

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这是先前提交的APPLYHATCH_PLUS的修改。 APPLYHATCH_PLUSC创建已定义图形的位图版本，其中用指定的彩色填充图案替换彩色元素。 APPLYHATCH_PLUSC允许为填充图案定义颜色。 可以使用char字符串或RGB值定义颜色。 提供了各种示例，显示了APPLYHATCH_PLUSC和MAKEHATCH_PLUS的一些可能性。 注意：此功能与Brandon Levey的APPLYHATCH_PLUSCOLOR不同，后者的功能和语法略有不同。

bbb优化 这里有一些优化和平稳运行BigBlueButton服务器的技术，包括提高记录处理速度，动态视频配置文件，分页，提高音频质量，修复1007/1020错误和使用apply-config.sh。 进行这些更改后，请不要忘记重新启动BigBlueButton服务器 bbb-conf --restart 管理自定义 将BigBlueButton服务器的所有自定义项保留在apply-config.sh中，以使（1）所有BBB服务器都具有相同的自定义项而没有任何错误，并且（2）升级时不会丢失它们。 我们使用XMLStarlet更新xml文件，并使用sed更新文本文件。 sudo apt-get update -y sudo apt-get install -y xmlstarlet git clone https://github.com/manishkatyan/bbb-optimi

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图神经网络是一种对没有固定结构的数据进行建模的诱人方法。然而，让他们按预期工作多年来经历了一些曲折。在本次演讲中，我将介绍图挖掘团队在谷歌上使GNN有用的工作。我将专注于我们已经发现的挑战以及我们为它们开发的解决方案。具体来说，我将重点介绍一些工作，这些工作实现了更富表现力的图卷积、更健壮的模型和更好的图结构。

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