![](./docs/assets/images/cover.png)

# FLAI: Reinforcement Learning Virtual Platform for Travel


![python](https://img.shields.io/badge/python-3.8-blue.svg)
[![version](https://img.shields.io/badge/version-0.1.2-green.svg)](http://deepair.io)
[![docs](https://img.shields.io/badge/link-docs-orange)](https://deepair-io.github.io/flai/#/)
## Introduction
Welcome to **Flai**, pronounced as 'Fly!' :smiley:


Flai is toolkit for developing and comparing reinforcement learning algorithms built by [deepair](https://www.deepair.io). It is inspired by OpenAI Gym and has been modified for travel's needs. Flai comes with pre packaged games that are designed to play by reinforcement learning agents. We are continuously developing and adding new environments to Flai.

## Installation
To install the entire library, use `pip install deepair-flai`.

This does not include dependencies for all families of environments (there's a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like `pip install deepair-flai[seatsmart]` or use `pip install deepair-flai[ubundle]` to install all dependencies.

We support Python 3.8 and above on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.

## Getting started
Coming soon..

## Documentation

Check out our documentation site [here](https://deepair-io.github.io/flai/#/).

> **Note**: Documentation is work in progress. Please feel free to raise issues or contribute to enhance the experience. 

### Local Server
Launching documentation server locally requires `npm`. It is built on `docsify` and recommended to install globally using the following command:

```
npm i docsify-cli -g
```

Now to run the server, you can run the following command:

```
docsify init ./docs
```
