Shen Mu Xin's Blog

Use Hexo + Github Build Your Blog 教程(三)客制化butterfly主题参考官方的配置教程每个主题都有自己的配置方法，官方都有详细的说明，可以参考官方的说明文档。这里以博主使用的butterfly主题为例。butterfly的官方配置教程在这里https://butterfly.js.org/ 我们的大部分修改要在主题文件的_config.yml和hexo目录中的_config.yml文件中进行。 修改Hexo配置文件在_config.yml中找到字段Site，修改基本信息 12345678# Sitetitle: 网站的标题subtitle: ''description: ''keywords:author: 网站作者名language: zh-CN # en # zh-TWtimezone: '' 修改主题配置文件在themes/butterfly/_config.yml进行修改，具体的修改方式可以参考官方的说明文档https://butterfly.js.org/。这里不再赘述了。 为 ...

Use Hexo + Github Build Your Blog 教程(二)配置github与自选主题配置github远端连接我们需要在Github上新建一个仓库来托管我们的网页，需要注意Github只能使用一个同名的仓库名来托管一个静态的站点，所以我新建的仓库名字应该和我们的用户名一致，比如我的用户名是shenmuxin，那么我的仓库名应该为shenmuxin.github.io，具体可以参考下图 然后我们需要配置SSH key，这样我们就可以通过SSH来连接Github了，具体操作如下 123git config --global user.name "your name"git config --global user.email "your email"ssh-keygen -t rsa -C "your email(和上面的邮箱一致)" 按照提示完成三次回车，便可以生成SSH key，可以采用以下的指令来查看自己生成的SSH key 1cat ~/.ssh/id_rsa.pub 也可以手动打开~/.ssh/id ...

Use Hexo + Github Build Your Blog 教程(一)搭建基本环境与基本操作环境配置首先笔者使用的操作系统环境为windows11,以下默认的安装环境为windows11。 安装Git首先下载Git，https://git-scm.com/download/win下载完成后直接安装即可。然后在git中进行基本配置，首先绑定自己的Github账号，鼠标右键点击Git Bash Here打开Git的终端，输入以下命令： 12git config --global user.name "your name"git config --global user.email "your email" 将相应位置替换为你自己的Github账号信息。配置完成之后可以使用 1git config --list 查看一下配置是否正确。 安装Node.js然后，下载Nodejs，https://nodejs.org/en下载完成后直接安装即可。安装完成之后，查看安装是否正确，同样用Git Bash Here打开终端，然后在终端中输入 123git ...

Ubuntu美化主题1. 安装优化工具Tweak首先安装优化工具Tweak 1sudo apt-get install gnome-tweak-tool 安装完毕后在菜单中打开Tweak 然后在extension中打开User Themes如下所示 如果apperenance中Shell部分出现了感叹号，则使用下面的语句安装依赖 1sudo apt-get install gnome-shell-extensions 2.下载自己喜欢的主题进入Gnome-look.org，依次进入GTK3/4 Themes，下载自己喜欢的主题。 笔者这里使用的主题是WhiteSur Gtk Theme 找到下载的地址，打开终端进行解压 1tar -xvf WhiteSur-Light.tar.xz 然后将解压后的文件拷贝到/usr/share/themes路径中 1sudo cp -ar WhiteSur-Light /usr/share/themes/ 然后我们就能在Tweak中看到这个主题了，在apperance中的applications进行修改即可 3. 下载自己喜欢的icon进入Gn ...

Reinforcement Learning with CodeThis note records how the author begin to learn RL. Both theoretical understanding and code practice are presented. Many material are referenced such as ZhaoShiyu’s Mathematical Foundation of Reinforcement Learning, . Chapter 5. Monte Carlo Learning​ What is Monte Carlo estimation? Monte Carlo estimation refers to a broad class of techniques that use stochastic samples to solve approximation problems using the Law of Large Numbers. ​ ChatGpt tells us that “Monte ...

Reinforcement Learning with CodeThis note records how the author begin to learn RL. Both theoretical understanding and code practice are presented. Many material are referenced such as ZhaoShiyu’s Mathematical Foundation of Reinforcement Learning, . Chapter 4. Value Iteration and Policy Iteration​ Value iteration and policy iteration have a common name called dynamic programming. Dynamic programming is model-based algorithm, which is the simplest RL algorithm. Its helpful to us to understand the ...

Reinforcement Learning with Code.This note records how the author begin to learn RL. Both theoretical understanding and code practice are presented. Many material are referenced such as ZhaoShiyu’s Mathematical Foundation of Reinforcement Learning, . Chapter 3. Optimal State Value and Bellman Optimality Equation3.1 How to define optimal​ One core idea is that we use the action value to judge the optimality of the action. If we update the policy to select the action with the greatest action value ...

Reinforcement Learning with Code.This note records how the author begin to learn RL. Both theoretical understanding and code practice are presented. Many material are referenced such as ZhaoShiyu’s Mathematical Foundation of Reinforcement Learning, . Chapter 2. State Value and Bellman Equation2.1 State value State value is defined as the mean of all possible returns starting from a state, which is actually the expectation of return from a specific state. The mathematical definition is as follow ...

Reinforcement Learning with Code.This note record how the author begin to learn RL. Both theoretical understanding and code practice are presented. Many material are referenced such as ZhaoShiyu’s Mathematical Foundation of Reinforcement Learning . Chapter 1. Basic Concepts1.1 State and action State describe the status of the agent with respect to the environment, denoted by $s$. State space is the set of all states, denoted by $\mathcal{S}={s_1, s_2,\dots,s_n}$. Action describe the action ...