Explorable reinforcement learning
Work in progress
Attempt at creating explorable explanation of basic reinforcement learning algorithms.
Based on: Fable Minimal App
This is a simple Fable app including an Elmish counter with as little configuration as possible. If you want to see a more complex app including commonly used F# tools like Paket or Fake, check the Fulma demo.
- dotnet SDK 2.1 or higher
- node.js with npm
- An F# editor like Visual Studio, Visual Studio Code with Ionide or JetBrains Rider.
Building and running the app
- Install JS dependencies:
- Start Webpack dev server:
- After the first compilation is finished, in your browser open: http://localhost:8080/
Any modification you do to the F# code will be reflected in the web page after saving.
JS dependencies are declared in
package-lock.json is a lock file automatically generated.
Webpack is a JS bundler with extensions, like a static dev server that enables hot reloading on code changes. Fable interacts with Webpack through the
fable-loader. Configuration for Webpack is defined in the
webpack.config.js file. Note this sample only includes basic Webpack configuration for development mode, if you want to see a more comprehensive configuration check the Fable webpack-config-template.
The sample only contains two F# files: the project (.fsproj) and a source file (.fs) in the
index.html file and other assets like an icon can be found in the