# 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](https://github.com/pbsinclair42/MCTS/releases), 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:
- `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:
```python
from mcts import mcts
mcts = mcts(timeLimit=1000)
bestAction = mcts.search(initialState=initialState)
```
See [naughtsandcrosses.py](https://github.com/pbsinclair42/MCTS/blob/master/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.
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"description": "# MCTS\n\nThis package provides a simple way of using Monte Carlo Tree Search in any perfect information domain. \n\n## Installation \n\nWith pip: `pip install mcts`\n\nWithout pip: Download the zip/tar.gz file of the [latest release](https://github.com/pbsinclair42/MCTS/releases), extract it, and run `python setup.py install`\n\n## Quick Usage\n\nIn 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: \n\n- `getPossibleActions()`: Returns an iterable of all actions which can be taken from this state\n- `takeAction(action)`: Returns the state which results from taking action `action`\n- `isTerminal()`: Returns whether this state is a terminal state\n- `getReward()`: Returns the reward for this state. Only needed for terminal states. \n\nYou 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. \n\nOnce these have been implemented, running MCTS is as simple as initializing your starting state, then running:\n\n```python\nfrom mcts import mcts\n\nmcts = mcts(timeLimit=1000)\nbestAction = mcts.search(initialState=initialState)\n```\nSee [naughtsandcrosses.py](https://github.com/pbsinclair42/MCTS/blob/master/naughtsandcrosses.py) for a simple example. \n\n## Slow Usage\n//TODO\n\n## Collaborating\n\nFeel 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.\n\n\n",
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