Lux-Design S3

Welcome to the Lux AI Challenge Season 3, an official NeurIPS 2024 competition!

The Lux AI Challenge is a competition where competitors design agents to tackle a multi-variable optimization, resource gathering, and allocation problem in a 1v1 scenario against other competitors. In addition to optimization, successful agents must be capable of analyzing their opponents and developing appropriate policies to get the upper hand.

Unique to this season is the introduction of partial observability and meta-learning style competition. A game against an opponent is won when the team wins a best of 5 match series where each match uses the same randomized map and game parameters.

The full game rules/specs can be found here.

Getting Started

We recommend using a python package manager like conda/mamba to install the dependencies.

mamba create -n "lux-s3" "python==3.11"
git clone https://github.com/Lux-AI-Challenge/Lux-Design-S3/
pip install -e Lux-Design-S3/src

To verify your installation, you can run a match between two random agents:

luxai-s3 --help

luxai-s3 path/to/bot/main.py path/to/bot/main.py --output replay.json

Then upload the replay.json to the online visualizer here: https://s3vis.lux-ai.org/ (a link on the lux-ai.org website will be up soon)

GPU Acceleration

Jax will already provide some decent CPU based parallelization for batch running the environment. A GPU or TPU however can increase the environment throughput much more however.

To install jax with GPU/TPU support, you can follow the instructions here.

To benchmark your throughput speeds, you can run

pip install pynvml psutil
python Lux-Design-S3/src/tests/benchmark_env.py -n 16384 -t 5 # 16384 envs, 5 trials each test

Starter Kits

Each supported programming language/solution type has its own starter kit.

The kits folder in this repository holds all of the available starter kits you can use to start competing and building an AI agent. The readme shows you how to get started with your language of choice and run a match. We strongly recommend reading through the documentation for your language of choice in the links below

• Python
• JS

Core Contributors

We like to extend thanks to the following users for their contributions to the Lux AI Season 3 competition: @jmerle (Visualizer)