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Contributing

Any and all contributions are more than welcome!

Running Tests

To develop for Catanatron, install the development dependencies and use the following test suite:

pip install .[web,gym,dev]
coverage run --source=catanatron -m pytest tests/ && coverage report

Or you can run the suite in watch-mode with:

ptw --ignore=tests/integration_tests/ --nobeep

Architecture

The code is divided in three main components (folders):

  • catanatron: The pure python implementation of the game logic. Uses networkx for fast graph operations. It is pip-installable (see pyproject.toml) and can be used as a Python package. See the documentation for the package here: https://catanatron.readthedocs.io/. The implementation of this follows the idea of Game Trees (see https://en.wikipedia.org/wiki/Game_tree) so that it lends itself for Tree-Searching Bots and Reinforcement Learning Environment Loops. Every "ply" is advanced with the .play_tick function. See more on Code Documentation site: https://catanatron.readthedocs.io/

    • catanatron.web: An extension package (optionally installed) that contains a Flask web server in order to serve game states from a database to a Web UI. The idea of using a database, is to ease watching games played in a different process. It defaults to using an ephemeral in-memory sqlite database. Also pip-installable with pip install catanatron[web].

    • catanatron.gym: Gymnasium interface to Catan. Includes a 1v1 environment against a Random Bot and a vector-friendly representations of states and actions. This can be pip-installed independently with pip install catanatron[gym], for more information see catanatron/gym/README.md.

  • catantron_experimental: A collection of unorganized scripts with contain many failed attempts at finding the best possible bot. Its ok to break these scripts. Its pip-installable. Exposes a catanatron-play command-line script that can be used to play games in bulk, create machine learning datasets of games, and more!

  • ui: A React web UI to render games. This is helpful for debugging the core implementation. We decided to use the browser as a randering engine (as opposed to the terminal or a desktop GUI) because of HTML/CSS's ubiquitousness and the ability to use modern animation libraries in the future (https://www.framer.com/motion/ or https://www.react-spring.io/).

Running Components Individually

As an alternative to running the project with Docker, you can run the web client and server in two separate tabs.

React App

cd ui/
npm install
npm start

This can also be run via Docker independently (after building):

docker build -t bcollazo/catanatron-react-ui:latest ui/
docker run -it -p 3000:3000 bcollazo/catanatron-react-ui

Flask Web Server

Ensure you are inside a virtual environment with all dependencies installed and use flask run. This will use SQLite by default.

pip install -e .[web]
FLASK_DEBUG=1 FLASK_APP=catanatron.web/catanatron.web flask run

This can also be run via Docker independently (after building):

docker build -t bcollazo/catanatron-server:latest . -f Dockerfile.web
docker run -it -p 5001:5001 bcollazo/catanatron-server

Useful Commands

These are other potentially useful commands while developing catanatron

TensorBoard

For watching training progress, use keras.callbacks.TensorBoard and open TensorBoard:

tensorboard --logdir logs

Docker GPU TensorFlow

docker run -it tensorflow/tensorflow:latest-gpu-jupyter bash
docker run -it --rm -v $(realpath ./notebooks):/tf/notebooks -p 8888:8888 tensorflow/tensorflow:latest-gpu-jupyter

Testing Performance

python -m cProfile -o profile.pstats catanatron/catanatron/cli/play.py --num=5
snakeviz profile.pstats
pytest --benchmark-compare=0001 --benchmark-compare-fail=mean:10% --benchmark-columns=min,max,mean,stddev

Head Large Datasets with Pandas

import pandas as pd
x = pd.read_csv("data/mcts-playouts-labeling-2/labels.csv.gzip", compression="gzip", iterator=True)
x.get_chunk(10)

Building Sphinx Code Documentation Site

pip install -r docs/requirements.txt
sphinx-quickstart docs
sphinx-apidoc -o docs/source catanatron
sphinx-build -b html docs/source/ docs/build/html

Publishing to PyPi (Outdated)

catanatron Package

make build PACKAGE=catanatron
make upload PACKAGE=catanatron
make upload-production PACKAGE=catanatron

catanatron_gym Package

make build PACKAGE=catanatron_gym
make upload PACKAGE=catanatron_gym
make upload-production PACKAGE=catanatron_gym

Ideas for Contribution

  • Improve catanatron package running time performance.

    • Continue refactoring the State to be more and more like a primitive dict or array. (Copies are much faster if State is just a native python object).

    • Move RESOURCE to be ints. Python enums turned out to be slow for hashing and using.

    • Move the .actions action log concept to the Game class. (to avoid copying when copying State)

    • Remove .current_prompt. It seems its redundant with (is_moving_knight, etc...) and not needed.

  • Improve AlphaBetaPlayer

    • Explore and improve prunning

    • Use Bayesian Methods or SPSA to tune weights and find better ones.

  • Research!

    • Deep Q-Learning

    • Simple Alpha Go

    • Try Tensorforce with simple action space.

    • Try simple flat CSV approach but with AlphaBeta-generated games.

  • Features

    • Continue implementing actions from the UI (not all implemented).

    • Chess.com-like UI for watching game replays (with Play/Pause and Back/Forward).

    • A terminal UI? (for ease of debugging)

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