Version: 0.1 (It's still alpha, don't use it for your production website!)
Website: https://github.com/hildensia/mcts
An implementation of Monte Carlo Search Trees in python.
Setup
Requirements:
- numpy
- scipy
- pytest for tests
Than plain simple python setup.py install. Or use pip: pip install scikit.mcts.
Usage
Assume you have a very simple 3x3 maze. An action could be 'up', 'down', 'left' or 'right'. You start at [0, 0] and there is a reward at [2, 2].
class MazeAction(object):
def __init__(self, move):
self.move = np.asarray(move)
def __eq__(self, other):
return all(self.move == other.move)
def __hash__(self):
return 10*self.move[0] + self.move[1]
class MazeState(object):
def __init__(self, pos):
self.pos = np.asarray(pos)
self.actions = [MazeAction([1, 0]),
MazeAction([0, 1]),
MazeAction([-1, 0]),
MazeAction([0, -1])]
def perform(self, action):
pos = self.pos + action.move
pos = np.clip(pos, 0, 2)
return MazeState(pos)
def reward(self, parent, action):
if all(self.pos == np.array([2, 2])):
return 10
else:
return -1
def is_terminal(self):
return False
def __eq__(self, other):
return all(self.pos == other.pos)
def __hash__(self):
return 10 * self.pos[0] + self.pos[1]
This would be a plain simple implementation. Now let's run MCTS on top:
mcts = MCTS(tree_policy=UCB1(c=1.41),
default_policy=immediate_reward,
backup=monte_carlo)
root = StateNode(parent=None, state=MazeState([0, 0]))
best_action = mcts(root)
Licence
See LICENCE
Authors
Johannes Kulick