# gym

**Repository Path**: thegreatwb/gym

## Basic Information

- **Project Name**: gym
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-04-09
- **Last Updated**: 2021-04-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Gym

[![Build Status](https://dev.azure.com/OpenNetLab/ONL-github/_apis/build/status/OpenNetLab.gym?branchName=master)](https://dev.azure.com/OpenNetLab/ONL-github/_build/latest?definitionId=6&branchName=master)

This gym leverages NS3 and WebRTC, which can be used by reinforcement learning or other methods to build a Bandwidth Controller for WebRTC. Here is an example [gym-example](https://github.com/OpenNetLab/gym-example) to use this Gym training a bandwidth estimator.

### Setup Guide

#### Get Gym

```sh
git clone https://github.com/OpenNetLab/gym gym
cd gym
```

#### Install dependencies(Ubuntu 18.04 or Ubuntu 20.04)

```sh
sudo apt install libzmq5 python3 python3-pip
python3 -m pip install -r requirements.txt
# Install Docker
curl -fsSL get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ${USER}
```

#### Download pre-compiled binary

If your OS is ubuntu18.04 or ubuntu20.04, we recommend you directly downloading pre-compiled binary, and please skip the step [Build Gym binary](#Build-Gym-binary)

The pre-compiled binary can be found from the latest [GithubRelease](https://github.com/OpenNetLab/gym/releases/latest/download/target.tar.gz). Please download and uncompress it in the current folder.
```
wget https://github.com/OpenNetLab/gym/releases/latest/download/target.tar.gz
tar -xvzf target.tar.gz
```

#### Build Gym binary

```sh
make init
make sync
make gym # build_profile=debug
```

If you want to build the debug version, try `make gym build_profile=debug`

#### Verify gym

```sh
python3 -m pytest alphartc_gym
```

### Interface description

You can use this Gym by a Python interface that was defined in [gym.py](alphartc_gym/gym.py)
### Inspiration

Thanks [SoonyangZhang](https://github.com/SoonyangZhang) provides the inspiration for the gym