This is an implementation of an AI in Python using the UCT Monte Carlo Tree Search algorithm.
The Monte Carlo Tree Search AIs included here are designed to work with jbradberry/boardgame-socketserver and jbradberry/boardgame-socketplayer.
You need to have the following system packages installed:
- Python >= 2.7
To set up your local environment you should create a virtualenv and install everything into it.
$ mkvirtualenv mcts
Pip install this repo, either from a local copy,
$ pip install -e mcts
or from github,
$ pip install git+https://github.com/jbradberry/mcts#egg=mcts
Additionally, you will need to have jbradberry/boardgame-socketplayer installed in order to make use of the players.
This project currently comes with two different Monte Carlo Tree
Search players. The first, jrb.mcts.uct, uses the count of the
number of wins for a node to make its decisions. The second,
jrb.mcts.uctv instead keeps track of the evaluated value of the
board for the playouts from a given node
$ board-play.py t3 jrb.mcts.uct # number of wins metric $ board-play.py t3 jrb.mcts.uctv # point value of the board metric
These AI players can also take additional arguments:
- time
- The amount of thinking time allowed for the AI to make its decision,
in seconds (default: 30). Ex:
$ board-play.py t3 jrb.mcts.uct -e time=5 - max_actions
- The maximum number of actions, or plays, to allow in one of the
simulated playouts before giving up (default: 1000). Ex:
$ board-play.py t3 jrb.mcts.uct -e max_actions=500 - C
- The exploration vs. exploitation coefficient at the heart of the UCT
algorithm (default: 1.4). Larger values prioritize exploring
inadequately covered actions from a node, smaller values prioritize
exploiting known higher valued actions. Experimentation with this
variable to find reasonable values for a given game is recommended.
Ex:
$ board-play.py t3 jrb.mcts.uct -e C=3.5
The -e flag may be used multiple times to set additional
variables.
Compatible games that have been implemented include: