agar DQN BOT

I have created a Agar Discreate Env enviroment, which can be used to create any type of RL Bot. The main framework is used the stable-baselines and it used to implement DQN bot for this game. (Stable baselines implement many other RL algoritms, so fell free to try them on this project.)

The idea of Enviroment

Our whole enviroment is build upon gym enviroment. At the initialization of the enviroment, both server and client of Agar are started. Step function returns number of frames, which was specified at the initialization. (This is necesery, because then out agent can recognize movement direction.)

Enviroment

The enviroment is based on two github repositories, both have little changes.

• Server side - Fork of OgarII - DQN requires discretize space, so we discretize the server with command next_step.
• Client side - This client side is fork from CigarII. We added some input hidden fields to index.html. These input fields are used fro communication between our python agent and server.

We use selenium to run webrowser and connect to our local webserver, which connects to Agar server.

Both server and client are coded in javascript, which is necessery to install.

At each step (also includes the reset step) multiple screeshots are taken of the opened selenium webrowser and return as the enviroment state.

Deatiled enviroment description can be find below.

Agar server settings

You can modify the world by editing settings.json file. They are multiple options you can play with as:

Installation

Docker Instalation (recomanded)

Download the git files and submodules

git clone https://github.com/lukaskiss222/agarDQNbot
cd agarDQBbot/
git submodule update --init --recursive

Install docker

docker pull scriptus/agarbot:latest

Run the docker with bash

docker run --rm -it -p 5900:5900 -p 6006:6006 -v $PWD:/home/agarDQNbot scriptus/agarbot /bin/bash 

How to see the training

If you want to see the enviroment then run training in the background by:

python main.py &
./connect_to_training.sh :1001

The last command will open xvfb to vnc port 5900 of your docker. Then on your localhost computer just run:

vncviewer  :0

and the window with the game should appear.

Testing model

If you want to test your model run:

python test_model.py models/<model>.zip &
./connext_to_training.sh :1001

as before run in yout local host vncviewer

Locahost instalation

Install Node and NPM

• Dependencies

sudo apt-get install curl
curl -sL https://deb.nodesource.com/setup_13.x | sudo -E bash -
sudo apt-get install nodejs
sudo apt-get update && sudo apt-get install cmake libopenmpi-dev python3-dev zlib1g-dev

Clone the project

git clone https://github.com/lukaskiss222/agarDQNbot
git submodule update --recursive

The selenium module requires geckodriver. Download it from https://github.com/mozilla/geckodriver/releases and untar it and copy it to /usr/bin/.

Install our python enviroment:

conda env create -f configure.yml
conda activate agarBot

Also we need to install necessery NPM packages to our client and server.

cd OgarII/
npm install
cd ../Cigar2/
npm install
cd ..

Check with this command, if everuthing is insstalled properly:

python EnviromentAgar.py

Somethimes node server is left running, so use killall node to kill server or client

Training

Run

python main.py

Detail enviroment description

• At the initialization of our gym enviroment, the Client webserver and agar webserver are started by subprocess and are left running until you close the gym enviroment. It is not recommended to multiple instances of our gym enviroment. (They will overlap. Moreover, you can not run mutiple servers on the same port.)
• .......

Help

Any help is welcome, this project is a answer to HW for machine learning class.

To do (Coming soon, hopefully :D)

• Future development
  • Moving enviroment to stable-baselines? (Maybe My DQN agent is not working)
  • Create own client based on Ws socket
    • Instead of images, it will get just server info by sockets
    • Maybe applicatble to normal Agar servers