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design for the first Lux AI Challenge","homepage":"https://github.com/Lux-AI-Challenge/LuxDesign#readme","keywords":["AI","Lux","Competition"],"repository":{"type":"git","url":"git+https://github.com/Lux-AI-Challenge/LuxDesign.git"},"author":{"name":"Lux-AI-Challenge"},"bugs":{"url":"https://github.com/Lux-AI-Challenge/LuxDesign/issues"},"license":"ISC","readme":"# Lux AI Challenge: Season 1\n\nThe night is dark and full of terrors. Two teams must fight off the darkness, collect resources, and advance through the ages. Daytime finds a desperate rush to gather and build the resources that can carry you through the impending night. Plan and expand carefully -- any city that fails to produce enough light will be consumed by darkness.\n\nWelcome to the Lux AI Challenge Season 1!\n\n<img src=\"./assets/daynightshift.gif\" style=\"width:50%\" />\n\nThe 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.\n\nTo get started, go to our [Getting Started](#getting-started) section. The competition runs until December 6th 2021 and submissions are due at 11:59PM UTC on the competition page: https://www.kaggle.com/c/lux-ai-2021\n\nThanks to our sponsors [QuantCo](https://quantco.com/), [J Ventures](http://www.thejiangmen.com/), and [QAImera](https://qaimera.com), we have a $10,000 prize pool along with many other non-monetary prizes this year! For more information see https://www.lux-ai.org/sponsors-2021\n\nMake 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.\n\nThis was built by the Lux AI Challenge team, using the [Dimensions](https://github.com/StoneT2000/Dimensions) package.\n\nSeason 1 specifications can be found here: https://lux-ai.org/specs-2021. These detail how the game works and what rules your agent must abide by.\n\n## Getting Started\n\nYou will need Node.js version 12 or above. See installation instructions [here](https://nodejs.org/en/download/), you can just download the recommended version.\n\nThe next parts detail the recommended setup to develop and compete your bot. For users who wish to use Python and Jupyter Notebooks / Kaggle Interactive Notebooks, feel free to skip this section and follow the [tutorial notebook](https://www.kaggle.com/stonet2000/lux-ai-season-1-jupyter-notebook-tutorial)\n\nOpen up the command line, and install the competition design with\n\n```\nnpm install -g @lux-ai/2021-challenge@latest\n```\n\nYou may ignore any warnings that show up, those are harmless. To run a match from the command line (CLI), simply run\n\n```\nlux-ai-2021 path/to/botfile path/to/otherbotfile\n```\n\nand the match will run with some logging and store error logs and a replay in a new `errorlogs` folder and `replays` folder. Logs stored in the errorlogs will include all error output and anything printed to standard error by your agent. You can watch the replay stored in the replays folder using our [visualizer](https://2021vis.lux-ai.org/). To watch the replay locally, follow instructions here https://github.com/Lux-AI-Challenge/LuxViewer2021/\n\nFor a full list of commands from the CLI, run\n\n```\nlux-ai-2021 --help\n```\n\nor go to the [next section](#CLI-Usage) to see more instructions on how to use the command line tool, including generating stateful replays and running local leaderboards for evaluation. You may also run this all in docker using the `cli.sh` file in this repo, see instructions [here](#cli-docker).\n\nEach programming language has a starter kit, you can find general API documentation here: https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/kits\n\nThe [kits](https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/kits) folder in this repository holds all of the available starter kits you can use to start competing and building an AI agent and show you how to get started with your language of choice and run a match with that bot can be found. **We strongly recommend reading through the documentation for your language of choice** in the links below\n\n- [Python](https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/kits/python)\n- [Javascript](https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/kits/js)\n- [Rust](https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/kits/rust) (maintained by [@tye-singwa](https://github.com/tye-singwa))\n- [C++](https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/kits/cpp)\n- [Java](https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/kits/java)\n- [Typescript](https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/kits/ts)\n- [Kotlin](https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/kits/kotlin) (maintained by [@Tolsi](https://github.com/Tolsi))\n\nThere are also many [community provided tools](#Community-Tools) to help people build better bots, feel free to check those out and use whatever suits your needs\n\nWant 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](https://github.com/Lux-AI-Challenge/Lux-Design-2021/blob/master/CONTRIBUTING.md) for more information on this.\n\nTo stay up to date on changes and updates to the competition and the engine, watch for announcements on the [forums](https://www.kaggle.com/c/hungry-geese/discussion) or the [Discord](https://discord.com/invite/DZSm47VHMz). See https://github.com/Lux-AI-Challenge/Lux-Design-2021/blob/master/ChangeLog.md for a full change log.\n\n## CLI Usage\n\n\n### CLI General \nThe CLI tool has several options. For example, one option is the seed and to set a seed of 100 simply run\n\n```\nlux-ai-2021 --seed=100 path/to/botfile path/to/otherbotfile\n```\n\nwhich will run a match using seed 100.\n\nYou can tell the CLI tool whether to store the agent logs or match replays via `--storeLogs, --storeReplay`. Set these boolean options like so\n\n```\n# to set to true\nlux-ai-2021 --statefulReplay\n# to set to false\nlux-ai-2021 --storeLogs=false\n```\n\nBy default the tool will generate minimum, **action-based**, replays that are small in size and work in the visualizer but it does not have state information e.g. resources on the map in each turn. To generate **stateful** replays, set the `--statefulReplay` option to true. To convert a action-based replay to a stateful one, set the `--convertToStateful` option to true and pass the file to convert.\n\nChoose where the replay file is stored at by setting `--out=path/to/file.json`\n\nYou can also change the logging levels by setting `--loglevel=x` for number x from 0 to 4. The default is 2 which will print to terminal all game warnings and errors.\n\n### CLI Leaderboard Evaluation\n\nYou can run your own local leaderboard / tournament to evaluate several bots at once via\n\n```\nlux-ai-2021 --rankSystem=\"trueskill\" --tournament path/to/agent1 path/to/agent2 path/to/agent3 path/to/agent4 ...\n```\n\nThis will run a leaderboard ranked by trueskill and print results as a table to your console. Agents are auto matched with opponents with similar ratings. Recommended to add `--storeReplay=false --storeLogs=false` as letting this run for a long time will generate a lot of replays and log files.\n\nSee `lux-ai-2021 --help` for more options.\n\n### CLI Docker\n\nThis tool matches the lux-ai-2021 exactly, but runs on Ubuntu 18.04, the target system that the competition servers use. Make sure to first [install docker](https://docs.docker.com/get-docker/)\n\nTo then use the lux-ai-2021 CLI tool, simply call `bash cli.sh` and it will accept the same exact arguments. On the first run, it will build a docker image and run a container in the background. Future runs will then be much faster. Moreover, this uses a bind mount, so you can edit files locally on your computer and they will be reflected in the docker container and vice versa. \n\nThe only caveat of this tool is that it has no access to files in directories above the current working directory (the output of the `pwd` command).\n\nMoreover, this tool will not inherit the same installed python packages on your computer. To add packages, please download the Dockerfile from this repo into the same directory as the `cli.sh` file and add installation commands like so to the bottom of the file\n\n```docker\nRUN pip3 install <package_name>\n```\n\nAfter changing the Dockerfile, run `bash cli.sh clean` to clean the old docker stuff and then use `cli.sh` as usual.\n\n## Community-Tools\n\nThis is a list of all community tools built by our community!\n\n- [Lux AI Python Gym](https://github.com/glmcdona/LuxPythonEnvGym/): A OpenAI compliant gym that replicates the entire Lux AI 2021 design. About 45x faster than the official environment for python agents and makes it easier to try more ML / search heavy approaches to the competition, maintained by [glmcdona](https://github.com/glmcdona/)\n- [Optics](https://github.com/rooklift/optics): A simple, fast, top-down-view visualizer by [rooklift](https://github.com/rooklift/)\n- [Command Line Visualizer](https://github.com/nathanbabcock/lux-ai-vis): A streamlined tool to watch a replay by running a single command in the CLI\n- [Public Notebooks](https://www.kaggle.com/c/lux-ai-2021/code): A collection of all public notebooks (in python usually) where competitors share their ideas, code, and more. There are all kinds of solutions here, from RL, Imitation Learning, to rule based approaches.\n- [Submission Statistics](https://lux-ai-stats.herokuapp.com/): A tool to track any submission to the competition, showing score change over time, wins and losses etc.\n\n\n## Contributing\n\nSee the [guide on contributing](https://github.com/Lux-AI-Challenge/Lux-Design-2021/tree/master/CONTRIBUTING.md)\n\n## Authors\n\nOriginal design for season 1 concevied by [Bovard](https://github.com/bovard) and [Stone](https://github.com/StoneT2000)\n\nUI/UX Design by [Isa](https://github.com/p-isa)\n\nWith balance testing help from [David](https://github.com/holypegasus)\n\n## Sponsors\nWe would like to thank our 3 sponsors, [QuantCo](https://quantco.com/), [J Ventures](http://www.thejiangmen.com/), and [QAImera](https://qaimera.com) this year for allowing us to provide a prize pool and exciting opportunities to our competitors! For more information on them, check them out here: https://www.lux-ai.org/sponsors-2021\n","readmeFilename":"README.md"}