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import math
from copy import deepcopy
from time import perf_counter
from random import choice
from GameState import GameState
# Based on https://github.com/DieterBuys/mcts-player/
class GameController(object):
def get_next_move(self, state):
# when you get a new move, it is assumed that the game is not ended yet
assert state.get_moves()
class MCTSNode(object):
"""Monte Carlo Tree Node.
Each node encapsulates a particular game state, the moves that
are possible from that state and the strategic information accumulated
by the tree search as it progressively samples the game space.
"""
def __init__(self, state, parent=None, move=None):
self.parent = parent
self.move = move
self.state = state
self.plays = 0
self.score = 0
self.pending_moves = state.get_moves()
self.children = []
def select_child_ucb(self):
# Note that each node's plays count is equal
# to the sum of its children's plays
def ucb(child):
win_ratio = child.score / child.plays \
+ math.sqrt(2 * math.log(self.plays) / child.plays)
return win_ratio
return max(self.children, key=ucb)
def expand_move(self, move):
self.pending_moves.remove(move) # raises KeyError
child_state = deepcopy(self.state)
child_state.play_move(move)
child = MCTSNode(state=child_state, parent=self, move=move)
self.children.append(child)
return child
def get_score(self, result):
# return result
if result == 0.5:
return result
if self.state.player == 2:
if self.state.next_turn_player == result:
return 0.0
else:
return 1.0
else:
if self.state.next_turn_player == result:
return 1.0
else:
return 0.0
if self.state.next_turn_player == result:
return 0.0
else:
return 1.0
def __repr__(self):
s = 'ROOT\n' if self.parent is None else ''
children_moves = [c.move for c in self.children]
s += """Score ratio: {score} / {plays}
Pending moves: {pending_moves}
Children's moves: {children_moves}
State:
{state}\n""".format(children_moves=children_moves, **self.__dict__)
return s
class MCTSGameController(GameController):
"""Game controller that uses MCTS to determine the next move.
This is the class which implements the Monte Carlo Tree Search algorithm.
It builds a game tree of MCTSNodes and samples the game space until a set
time has elapsed.
"""
def select(self):
node = self.root_node
# Descend until we find a node that has pending moves, or is terminal
while node.pending_moves == set() and node.children != []:
node = node.select_child_ucb()
return node
def expand(self, node):
assert node.pending_moves != set()
move = choice(tuple(node.pending_moves))
return node.expand_move(move)
def simulate(self, state, max_iterations=1000):
state = deepcopy(state)
move = state.get_random_move()
while move is not None:
state.play_move(move)
move = state.get_random_move()
max_iterations -= 1
if max_iterations <= 0:
return 0.5 # raise exception? (game too deep to simulate)
return state.game_result
def update(self, node, result):
while node is not None:
node.plays += 1
node.score += node.get_score(result)
node = node.parent
def get_next_move(self, state, time_allowed=1.0):
super(MCTSGameController, self).get_next_move(state)
self.root_node = MCTSNode(state)
iterations = 0
start_time = perf_counter()
while perf_counter() < start_time + time_allowed:
node = self.select()
if node.pending_moves != set():
node = self.expand(node)
result = self.simulate(node.state)
self.update(node, result)
iterations += 1
# Return most visited node's move
return max(self.root_node.children, key=lambda n: n.plays).move
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