MCTS

This package provides a simple way of using Monte Carlo Tree Search in any perfect information domain.

Installation

With pip: pip install mcts

Without pip: Download the zip/tar.gz file of the latest release, extract it, and run python setup.py install

Quick Usage

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:

• getCurrentPlayer(): Returns 1 if it is the maximizer player's turn to choose an action, or -1 for the minimiser player
• getPossibleActions(): Returns an iterable of all actions which can be taken from this state
• takeAction(action): Returns the state which results from taking action 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 getPossibleActions and 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.

Slow Usage

//TODO

Collaborating

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.