Hi! I'm QLara.

Hello everyone! My name is QLara. I am a LuxAI agent gamer based in Reinforcement Learning. I'm here to show you how I am programmed and to get you started on Q-Learning.

Before going any further into myself, I'm programmed to play the Lux Kaggle Competition. You can read all competition rules, this may be a bit of a learning curve and you can skip it if you are only interested in me but make sure to read it before modify my behaviour.

What will you find in this project?

A Reinforcement Learning agent to be trained to play LuxAI Challenge. Our trained model is not included in the repository. But you can train it on your own, in your local machine using the script agent.py

$ python agent.py

You can configure the training parameters in this file by editing the configurationvariable:

configuration = {
    'lr': 0.01,
    'gamma': 0.9,
    'epsilon': 0.9,
    'epsilon_final': 0.5,
    'epsilon_decay': 0.995,
    'batch_length': 1,
    'epochs': 0,
    'episodes': 0
}

Play the game!

Do you want to play a game? The Lux repository has a detailed guide on installing dependencies. Here are the main steps:

• python >=3.7
• nodeJS >=12
• luxAI node package. Installation:

$ npm install -g @lux-ai/2021-challenge@latest

• all packages in requierements.txt. Installation:

$ pip install -r requirements.txt

After installing all dependencies open the visualizer.ipynb to run and watch games!

$ jupyter notebook visualizer.ipynb

This will give you the visualizer to watch the game and minimally interact with the play such as this

The seed parameter will give you different maps and you can see how the map affects the implemented behaviour.

env = make("lux_ai_2021", configuration={"seed": 666, "loglevel": 0, "annotations": True}, debug=True)
env.render(mode="ipython", width=1000, height=800)

Play using Docker!

Of course, you can ignore all issues with dependencies if you have Docker installed. You can jump directly into visualizing a game.

First, you'll need to build the image.

docker build -t qlara .

Now, you can launch Jupyter in your browser by running a container.

docker run -v $(pwd):/root -p 8888:8888 -it --rm qlapa:latest

References (Spanish talk)

• https://youtu.be/NiixJV3c7f4?t=3878