LuxPythonEnvGym

This is a replica of the Lux AI 2021 game ported directly over to python. It also sets up a classic Reinforcement Learning gym environment to be used to train RL agents for creating agents.

Features	LuxAi2021
Lux game engine porting to python	✔️
Documentation	❌
All actions supported	✔️
PPO example training agent	✔️
Example agent converges to a good policy	✔️
Kaggle submission format agents	✔️
Lux replay viewer support	✔️
Game engine consistency validation to base game	❌

Installation

This should work cross-platform, but I've only tested Windows 10 and Ubuntu.

Important: Use Python 3.7.* for training your models. This is required since when you create a Kaggle submission, the Kaggle competition will run the code using Python 3.7.*, and you will get a model deserialization error if you train the model with Python 3.8>=.

Install luxai2021 environment package by running the installer:

python setup.py install

You will need Node.js version 12 or above: here

Example python ML training

Create your own agent logic, observations, actions, and rewards by modifying this example:

https://github.com/glmcdona/LuxPythonEnvGym/blob/main/examples/agent_policy.py

Then train your model by:

python ./examples/train.py

You can then run tensorboard to monitor the training:

tensorboard --logdir lux_tensorboard

Example kaggle notebook

Here is a complete training, inference, and kaggle submission example in Notebook format:

https://www.kaggle.com/glmcdona/lux-ai-deep-reinforcement-learning-ppo-example

Preparing a kaggle submission

You have trained a model, and now you'd like to submit it as a kaggle submission. Here are the steps to prepare your submission.

Either view the above kaggle example or prepare a submission yourself:

1. Place your trained model file as model.zip and your agent file agent_policy.py in the ./kaggle_submissions/ folder.
2. Run python download_dependencies.py in ./kaggle_submissions/ to copy two required python package dependencies into this folder (luxai2021 and stable_baselines3).
3. Tarball the folder into a submission tar -czf submission.tar.gz -C kaggle_submissions .

Important: The model.zip needs to have been trained on Python 3.7.* or you get a deserialization error, since this is the python version that Kaggle Environment uses to inference the model in submission.

Creating and viewing a replay

Place your trained model file as model.zip and your agent file agent_policy.py in the ./kaggle_submissions/ folder. Then run a command like the following from that directory:

lux-ai-2021 --seed=100 ./kaggle_submissions/main_lux-ai-2021.py ./kaggle_submissions/main_lux-ai-2021.py --maxtime 100000

This will battle your agent against itself and produce a replay match. You can view the replay here: https://2021vis.lux-ai.org/