A simple package to allow users to run Monte Carlo Tree Search on any perfect information domain
This package provides a simple way of using Monte Carlo Tree Search in any perfect information domain.
pip install mcts
Without pip: Download the zip/tar.gz file of the latest release, extract it, and run
python setup.py install
In order to run MCTS, you must implement a
State class which can fully describe the state of the world. It must also implement four methods:
getPossibleActions(): Returns an iterable of all actions which can be taken from this state
takeAction(action): Returns the state which results from taking action
isTerminal(): Returns whether this state is a terminal state
getReward(): Returns the reward for this state. Only needed for terminal states.
You must also choose a hashable representation for an action as used in
takeAction. Typically this would be a class with a custom
__hash__ method, but it could also simply be a tuple or a string.
Once these have been implemented, running MCTS is as simple as initializing your starting state, then running:
from mcts import mcts mcts = mcts(timeLimit=1000) bestAction = mcts.search(initialState=initialState)
See naughtsandcrosses.py for a simple example.
Feel free to raise a new issue for any new feature or bug you've spotted. Pull requests are also welcomed if you're interested in directly improving the project.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size mcts-1.0.4-py3-none-any.whl (4.2 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size mcts-1.0.4.tar.gz (3.2 kB)||File type Source||Python version None||Upload date||Hashes View|