Skip to main content

The Lux AI Challenge Season 2

Project description

Lux-Design-2022

PyPI version

Welcome to the Lux AI Challenge Season 2!

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.

We are currently in beta, so expect unpolished parts and bugs of the engine and visuals.

To get started, go to our Getting Started section. The Beta competition runs until December 6 and submissions are due at 11:59PM UTC on the competition page: https://www.kaggle.com/c/lux-ai-2022-beta/

Make sure to join our community discord at https://discord.gg/aWJt3UAcgn to chat, strategize, and learn with other competitors! We will be posting announcements on the Kaggle Forums and on the discord.

Season 2 specifications can be found here: https://lux-ai.org/specs-2022-beta. These detail how the game works and what rules your agent must abide by.

Interested in Season 1? Check out last year's repository where we received 22,000+ submissions from 1,100+ teams around the world ranging from scripted agents to Deep Reinforcement Learning.

Getting Started

You will need Python >=3.7, <3.11 installed on your system. Once installed, you can install the Lux AI season 2 environment with

pip install --upgrade luxai2022

To verify your installation, you can run the CLI tool by replacing path/to/bot/main.py with a path to a bot (e.g. the starter kit in kits/python/main.py) and run

luxai2022 path/to/bot/main.py path/to/bot/main.py -v 2 -o replay.json

This will turn on logging to level 2, and store the replay file at replay.json. For documentation on the luxai2022 tool, see https://github.com/Lux-AI-Challenge/Lux-Design-2022/tree/main/luxai_runner/README.md, which includes details on how to run a local tournament to mass evaluate your agents.

Each programming language has a starter kit, you can find general API documentation here: https://github.com/Lux-AI-Challenge/Lux-Design-2022/tree/main/kits

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
  • C++
  • Javascript - TBA
  • Typescript - TBA
  • Java - TBA

Want to use another language but it's not supported? Feel free to suggest that language to our issues or even better, create a starter kit for the community to use and make a PR to this repository. See our CONTRIBUTING.md document for more information on this.

To stay up to date on changes and updates to the competition and the engine, watch for announcements on the forums or the Discord. See ChangeLog.md for a full change log.

Community Tools

As the community builds tools for the competition, we will post them here!

3rd Party Viewer - https://github.com/jmerle/lux-eye-2022

Contributing

See the guide on contributing

Sponsors

To be announced at the official release.

Core Contributors

We like to extend thanks to some of our early core contributors: @duanwilliam (Frontend), @programjames (Map generation, Engine optimization), and @themmj (C++ kit, Engine optimization).

We further like to extend thanks to contributors during the beta period: @LeFiz (Game Design/Architecture).

Citation

If you use the Lux AI Season 2 environment in your work, please cite this repository as so

@software{Lux_AI_Challenge_S1,
  author = {Tao, Stone and Doerschuk-Tiberi, Bovard},
  month = {10},
  title = {{Lux AI Challenge Season 2}},
  url = {https://github.com/Lux-AI-Challenge/Lux-Design-2022},
  version = {1.0.0},
  year = {2022}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

luxai2022-1.1.1.tar.gz (44.4 kB view details)

Uploaded Source

Built Distribution

luxai2022-1.1.1-py3-none-any.whl (54.2 kB view details)

Uploaded Python 3

File details

Details for the file luxai2022-1.1.1.tar.gz.

File metadata

  • Download URL: luxai2022-1.1.1.tar.gz
  • Upload date:
  • Size: 44.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.15

File hashes

Hashes for luxai2022-1.1.1.tar.gz
Algorithm Hash digest
SHA256 d20abac220d2440a283d7aacf6bc88ce0a9929e60a317a9c7240b9b7f6cd527a
MD5 c409c80a3f722d1fcf3c7617a5a0d8d7
BLAKE2b-256 4e7b85a44375a42b6584351293358f3da0cb226c2e0b9b38fd5c28c9a7455469

See more details on using hashes here.

File details

Details for the file luxai2022-1.1.1-py3-none-any.whl.

File metadata

  • Download URL: luxai2022-1.1.1-py3-none-any.whl
  • Upload date:
  • Size: 54.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.15

File hashes

Hashes for luxai2022-1.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 0985f739c46caf0f1c31df3d916d24d6dad3b798a6a08c31a7b19969981156fa
MD5 7712e80a723b55c0c3d292dcc938676a
BLAKE2b-256 aab62732c43b9231aca334eee70ced5846059fa4d4d7ef58fd3c3f406a8401f2

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page