Snake AI

game-example.mp4

For this project, my idea was to explore some algorithms, such as genetic algorithms and Qubo, and technologies, like C++, to develop something I've always wanted to, all from scratch!

The urge to build a snake game AI came after I watched some YouTube videos, which inspired me to create something different and just for fun, some of them are listed bellow:

Technologies

The Technologies used were:

• git
• SDL2
• C++
• make/cmake
• googletest (ctest)
• sh
• Github actions
• python 3.10 (used to test the QUBO model)
• pygame
• docker
• docker compose

Different modes

In this version of snake game, I've implemented 3 game modes:

Play as human

As the classic one, your can play and enjoy the game by yourself, without any machine inteligent agent.

PRESS D to start playing and use the WASD to move the snake around.

player.mp4

AI

In the AI version, you have 2 screens to select either train the AI or let the AI play.

Train AI

To train the AI PRESS A and let her learn to play. If you want you can PRESS S during the training to save the best individual weights, but after each generation a weights file is save with the best one. If you want to go back from the training module, you can also PRESS R to return to the main Screen.

ai.mp4

Let the AI play

After acquaring the weights file (.wg), you can put them into an AI agent to play. To do that, just PRESS S at the main SCreen, and import your .wg file.

QUBO

The last mode, is based on QUBO model. This one has a mathematical an expression with some binary variables, and the goal is to minimize it using different combinations for these binary variables.

$$\sum_{i = 0}^{2}{Q_{ii}x_{i}} + P((\sum_{i = 0}^{2}{x_{i}})-1)^2$$

In this expression, $Q$ is a matrix with the distances between the player and the food, which:

Q[0][0] = distance if the player go foward (in the same direction he was)
Q[1][1] = distance if the player go down/right 
Q[2][2] = distance if the player go up/left

anything else are just zeros

$x_i$ is the binary variable $i$. $P$ is a penality value, in this case we used $(window_{height}*window_{width}) + 10$, the penality part ensures that only one movement will be passed as outcome (either $001$, $010$ or $100$).

To play this one, PRESS Q at the start Screen.

qubo.mp4

Usage

All the following usage ways are focused on Ubuntu based distros, so some steps may differ for different OS. Remember to check the tools documentation for your system.

Docker

The simplest way to run it, is by using Docker.

First pull the image:

docker pull dpbm32/snake-ai

# or using the GHRC version
docker pull ghcr.io/dpbm/snake-ai:latest

Then you must grant access to your XDisplay.

xhost +local:root

# remeber revoking access after using it
xhost -local:root

Also, setup a docker volume for the neural network weights output.

docker volume create weights-out

Finally run the image:

docker run -v /tmp/.X11-unix:/tmp/.X11-unix:ro \
           -v /path/to/your/weights/input/folder/optional/:/snake/weights_input/ \
           --mount source=weights-out,target=/snake/weights_output/ \
           -e DISPLAY=$DISPLAY \
           -e WPATH=/snake/weights_output/ \
           snake-ai

Docker build local

Another way to do that is build the image by yourself. To do that run:

docker build -t snake-ai .

Then follow the steps after the docker pull from the #docker section.

Docker compose

Finally, There's a compose file in the project directory that you can use to orchestrate the image requirements.

After creating a volume and giving the XDisplay permissions, run:

docker compose up

Dev Build

To build the and run the project, you must the following tools and libraries installed:

• git
• SDL2
• C++
• make
• cmake
• sh

Then, clone the project and run the compilation script:

git clone https://github.com/Dpbm/snake-ai
cd ./snake-ai
chmod +x ./compile.sh ./clean.sh
./clean.sh && ./compile.sh main.cpp

Finally, run the game:

LC_NUMERIC="C" ./build/snake

# or, if you want to set a different path to the output weights
LC_NUMERIC="C" WPATH="/path/to/save/the/weights/" ./build/snake

Tests

In case you want to run the tests by your own, do the following:

cd build
ctest

Qubo test

Finally, inside this repo, there's a subproject made in python to test the Qubo model and how we could use it to play snake game. To access this piece of software:

Install the dependencies:

pip install -r requirements.txt #use python>=3.10

#or using conda (recommended)

# in case you don't have conda-lock installed
# pip install conda-lock 
conda-lock install -n snake-ai conda-lock.yml
conda activate snake-ai

and run:

python ./pygame-qubo-test/main.py

pyqubo.mp4

If you want to understand how the tests to map the qubo version were done, you can do:

# in case you used pip before
# pip install -r requirements-dev.txt

jupyter lab qubo-test.ipynb

Credits

Fonts were taken from: CodeMan38 Google fonts
nativefiledialog-extended by btzy