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PlayerAI_3.py
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PlayerAI_3.py
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from random import randint
from BaseAI_3 import BaseAI
import math
import time
start = 0
class PlayerAI(BaseAI):
def __init__(self):
self.possibleNewTiles = [2, 4]
self.probability = 0.9
self.time_limit = 0.1
self.smoothnessWeight = 0.1
self.monotonicityWeight = 1.0
self.emptyWeight = 2.7
self.maxWeight = 1.0
self.distanceWeight = 10.0
def evaluate(self, grid):
empty_cells = len(grid.getAvailableCells())
max_tile = grid.getMaxTile()
smoothness = self.smoothness(grid) * self.smoothnessWeight
monotonicity = self.monotonicity(grid) * self.monotonicityWeight
emptiness = (math.log(empty_cells) / math.log(2)) * self.emptyWeight if empty_cells != 0 else 0
maxvalue = self.get_max_value(max_tile, empty_cells) * self.maxWeight
distance = self.distance(grid, max_tile) * self.distanceWeight
return emptiness + monotonicity + smoothness + maxvalue + distance
@staticmethod
def distance(grid, max_tile):
dis = None
for x in range(grid.size):
if dis:
break
for y in range(grid.size):
if max_tile == grid.map[x][y]:
if max_tile < 1024:
dis = -((abs(x - 0) + abs(y - 0)) * max_tile)
else:
dis = -((abs(x - 0) + abs(y - 0)) * (max_tile / 2))
break
return dis
@staticmethod
def get_max_value(max_tile, empty_cells):
return math.log(max_tile) * empty_cells / math.log(2)
@staticmethod
def monotonicity(grid):
totals = [0, 0, 0, 0]
for x in range(3):
currentIndex = 0
nextIndex = currentIndex + 1
while nextIndex < 4:
while nextIndex < 4 and grid.map[x][nextIndex] == 0:
nextIndex += 1
if nextIndex >= 4:
nextIndex -= 1
currentValue = math.log(grid.map[x][currentIndex]) / math.log(2) if grid.map[x][currentIndex] else 0
nextValue = math.log(grid.map[x][nextIndex]) / math.log(2) if grid.map[x][nextIndex] else 0
if currentValue > nextValue:
totals[0] += currentValue + nextValue
elif nextValue > currentValue:
totals[1] += currentValue - nextValue
currentIndex = nextIndex
nextIndex += 1
for y in range(3):
currentIndex = 0
nextIndex = currentIndex + 1
while nextIndex < 4:
while nextIndex < 4 and grid.map[nextIndex][y] == 0:
nextIndex += 1
if nextIndex >= 4:
nextIndex -= 1
currentValue = math.log(grid.map[currentIndex][y]) / math.log(2) if grid.map[currentIndex][y] else 0
nextValue = math.log(grid.map[nextIndex][y]) / math.log(2) if grid.map[nextIndex][y] else 0
if currentValue > nextValue:
totals[2] += nextValue - currentValue
elif nextValue > currentValue:
totals[3] += currentValue - nextValue
currentIndex = nextIndex
nextIndex += 1
return max(totals[0], totals[1]) + max(totals[2], totals[3])
@staticmethod
def smoothness(grid):
smoothness = 0
for x in range(grid.size):
for y in range(grid.size):
s = float('infinity')
if x > 0:
s = min(s, abs((grid.map[x][y] or 2) - (grid.map[x - 1][y] or 2)))
if y > 0:
s = min(s, abs((grid.map[x][y] or 2) - (grid.map[x][y - 1] or 2)))
if x < 3:
s = min(s, abs((grid.map[x][y] or 2) - (grid.map[x + 1][y] or 2)))
if y < 3:
s = min(s, abs((grid.map[x][y] or 2) - (grid.map[x][y + 1] or 2)))
smoothness -= s
return smoothness
def get_new_tile(self):
if randint(0, 99) < 100 * self.probability:
return self.possibleNewTiles[0]
else:
return self.possibleNewTiles[1]
def search(self, grid, alpha, beta, depth, player):
if time.clock() - start > self.time_limit:
return self.evaluate(grid), -1, True
if depth == 0:
return self.evaluate(grid), -1, False
if player:
best_score, best_move = alpha, None
positions = grid.getAvailableMoves()
if len(positions) == 0:
return self.evaluate(grid), None, False
for position in positions:
new_grid = grid.clone()
new_grid.move(position)
score, move, timeout = self.search(new_grid, alpha, beta, depth - 1, False)
if score > best_score:
best_score, best_move = score, position
if best_score >= beta:
break
if best_score > alpha:
alpha = best_score
return best_score, best_move, False
else:
best_score, best_move = beta, None
cells = grid.getAvailableCells()
if len(cells) == 0:
return self.evaluate(grid), None, False
for cell in cells:
value = self.get_new_tile()
new_grid = grid.clone()
new_grid.setCellValue(cell, value)
score, move, timeout = self.search(new_grid, alpha, beta, depth - 1, True)
if score < best_score:
best_score, best_move = score, None
if best_score <= alpha:
break
if best_score < beta:
beta = best_score
return best_score, None, False
def iterative(self, grid):
global start
best_score, depth = -float('infinity'), 1
start = time.clock()
while True:
score, move, timeout = self.search(grid, -float('infinity'), float('infinity'), depth, True)
if timeout:
break
if score > best_score:
best_move, best_score = move, score
depth += 1
return best_move
def getMove(self, grid):
return self.iterative(grid)