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hnu202309010107 6 months ago
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AI_map.py
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import random
import map_game
import copy
import math
class AIMap:
def __init__(self,boardMap: list = None):
self.map = [[0 for i in range(4)] for i in range(4)] #初始化 全部清零
self.map = [[boardMap[i][j]for i in range(4)] for j in range(4)] #复制
#AI:向左转！
def ai_go_left(self):
isAdd = 0 # 表示是否成功移动
for row in range(0,4):
index = 0
for col in range(1,4):
if self.map[row][col] > 0:
if self.map[row][col] == self.map[row][index]:
self.map[row][index] = self.map[row][col] + self.map[row][index]
self.map[row][col] = 0
index += 1
isAdd = 1
elif self.map[row][index] == 0:
self.map[row][index] = self.map[row][col]
self.map[row][col] = 0
isAdd = 1
else:
index += 1
if self.map[row][index] == 0:
self.map[row][index] = self.map[row][col]
self.map[row][col] = 0
isAdd = 1
return isAdd
#AI:向右转！
def ai_go_right(self):
isAdd = 0 # 表示是否成功移动
for row in range(0,4):
index = 3
for col in range(2,-1,-1):
if self.map[row][col] > 0:
if self.map[row][col] == self.map[row][index]:
self.map[row][index] = self.map[row][col] + self.map[row][index]
self.map[row][col] = 0
index -= 1
isAdd = 1
elif self.map[row][index] == 0:
self.map[row][index] = self.map[row][col]
self.map[row][col] = 0
isAdd = 1
else:
index -= 1
if self.map[row][index] == 0:
self.map[row][index] = self.map[row][col]
self.map[row][col] = 0
isAdd = 1
return isAdd
#AI:向前进
def ai_go_up(self):
isAdd = 0 # 表示是否成功移动
for col in range(0,4):
index = 0
for row in range(1,4):
if self.map[row][col] > 0:
if self.map[row][col] == self.map[index][col]:
self.map[index][col] = self.map[row][col] + self.map[index][col]
self.map[row][col] = 0
index += 1
isAdd = 1
elif self.map[index][col] == 0:
self.map[index][col] = self.map[row][col]
self.map[row][col] = 0
isAdd = 1
else:
index += 1
if self.map[index][col] == 0:
self.map[index][col] = self.map[row][col]
self.map[row][col] = 0
isAdd = 1
return isAdd
#AI:向后退
def ai_go_down(self):
isAdd = 0 # 表示是否成功移动
for col in range(0,4):
index = 3
for row in range(2,-1,-1):
if self.map[row][col] > 0:
if self.map[row][col] == self.map[index][col]:
self.map[index][col] = self.map[row][col] + self.map[index][col]
self.map[row][col] = 0
index -= 1
isAdd = 1
elif self.map[index][col] == 0:
self.map[index][col] = self.map[row][col]
self.map[row][col] = 0
isAdd = 1
else:
index -= 1
if self.map[index][col] == 0:
self.map[index][col] = self.map[row][col]
self.map[row][col] = 0
isAdd = 1
return isAdd
#控制AI走不同的方向 走成功为True 走失败为False
def ai_go(self,dir):#0左 1右 2上 3下
if dir == 0:#左
if self.ai_go_left() == 1:
return True
else:
return False
elif dir == 1:#右
if self.ai_go_right() == 1:
return True
else:
return False
elif dir == 2:#上
if self.ai_go_up() == 1:
return True
else:
return False
elif dir == 3:#下
if self.ai_go_down() == 1:
return True
else:
return False
#数值分析
#格局评价
#作为算法的核心，如何评价当前格局的价值是重中之重。
#在2048中，除了终局外，中间格局并无非常明显的价值评价指标，因此需要用一些启发式的指标来评价格局。
#那些分数高的好格局是容易引向胜利的格局，而分低的坏格局是容易引向失败的格局。
def ai_evaluation(self):
#参数需要进行调试
#否则会变成人工智障
#smoothWeight = 0.5
#monoWeight = 0.03
#emptyWeight = 2.7
#maxWeight = 4
#disWeight = 10
#人工智障2.0参数
smoothWeight = 0.5
monoWeight = 0.03
emptyWeight = 2.7
maxWeight = 4
disWeight = 10
result = [disWeight*math.log2(self.dis_weight()), smoothWeight * self.smothness(), monoWeight * self.monotonicity(),
emptyWeight*math.log2(self.empty_num() + 1), maxWeight*self.max_and_submax_num()]
#人工智障3.0参数
#smoothWeight = 0.1 # 0.5
#monoWeight = 1.0 # 0.03
#emptyWeight = 2.7 # 2.7
#maxWeight = 1 # 0.01
#disWeight = 0
#smoothWeight = 0.5
#monoWeight = 0.03
#emptyWeight = 2.7
#maxWeight = 4
#disWeight = 10
#result = [disWeight*self.dis_weight(), smoothWeight * self.smothness(), monoWeight *
# self.monotonicity(), emptyWeight*math.log(self.empty_num()), maxWeight*self.max_num()]
return result
#单调性
#单调性指方块从左到右、从上到下均遵从递增或递减。
##一般来说，越单调的格局越好。
#下面是一个具有良好单调格局的例子
# 8 32 64 512
# 4 8 16 256
# 2 4 8 32
# 0 0 4 8
def monotonicity(self):
totals = [0,0,0,0] # 四个方向的单调性评估 左 右 上 下（单调递增）
#1.横着
for i in range(4):
current = 0
next = current+1
while next < 4:
while next < 4 and self.map[i][next] == 0:
next += 1
if next >= 4:
next -= 1
if self.map[i][current] != 0:
currentValue = math.log2(self.map[i][current])
else:
currentValue = 0
if self.map[i][next] != 0:
nextValue = math.log2(self.map[i][next])
else:
nextValue = 0
if currentValue > nextValue:
totals[0] += nextValue-currentValue
else:
totals[1] += currentValue-nextValue
current = next
next += 1
#2.竖着
for i in range(4):
current = 0
next = current+1
while next < 4:
while next < 4 and self.map[next][i] == 0:
next += 1
if next >= 4:
next -= 1
if self.map[current][i] != 0:
currentValue = math.log2(self.map[current][i])
else:
currentValue = 0
if self.map[next][i] != 0:
nextValue = math.log2(self.map[next][i])
else:
nextValue = 0
if currentValue > nextValue:
totals[2] += nextValue-currentValue
else:
totals[3] += currentValue-nextValue
current = next
next += 1
return max(totals[:2])+max(totals[2:])
#平滑度
#平滑性是指每个方块与其直接相邻方块数值的差，其中差越小越平滑。
#例如2旁边是4就比2旁边是128平滑。
#一般认为越平滑的格局越好。
#下面是一个具有极端平滑性的例子
# 1024 1024 1024
# 1024 1024 1024 1024
# 1024 1024 1024 1024
# 1024 1024 1024 1024
def smothness(self):
lubricity = 0
for i in range(4):
for j in range(4):
if self.map[i][j] != 0:
if i >= 1:
# 左边减去该值
lubricity -= abs(math.log2(self.map[i-1][j]+1) - math.log2(self.map[i][j]+1))
if i < 3:
# 右边减去该值
lubricity -= abs(math.log2(self.map[i+1][j]+1) - math.log2(self.map[i][j]+1))
if j > 0:
# 上面减去该值
lubricity -= abs(math.log2(self.map[i][j-1]+1) - math.log2(self.map[i][j]+1))
if j < 3:
# 下面减去该值
lubricity -= abs(math.log2(self.map[i][j+1]+1) - math.log2(self.map[i][j]+1))
return lubricity
#空格数
#因为一般来说，空格子越少对玩家越不利。
#空格越多的格局越好。
def empty_num(self):
empty_num = 0
for i in range(4):
for j in range(4):
if self.map[i][j] == 0:
empty_num += 1
return empty_num
#单个最大的值
def max_num(self):
max_num = 0
for i in range(4):
for j in range(4):
if max_num < self.map[i][j]:
max_num = self.map[i][j]
return max_num
#这个指标评价棋盘的单个数的最大值和总体和
#值越大格局越好
def max_and_submax_num(self):
max_num = 0
subMaxNum = 0
for row in range(4):
for col in range(4):
if max_num < self.map[row][col]:
subMaxNum = max_num + subMaxNum;
max_num = self.map[row][col]
return math.log2(subMaxNum/3+max_num*2/3) #2.0
#return subMaxNum/3+max_num*2/3 #3.0
#分布位置权重
#整体分布越往下，越往右格局越好
def dis_weight(self):
#wei = [[12, 13, 25, 50],
# [11, 10, 9, 8],
# [4, 5, 6, 7],
# [3, 2, 1, 0]]
wei = [[3, 2, 1, 0],
[4, 5, 6, 7],
[16, 10, 9, 8],
[32,64,128,256]]
dis_sum = 0
for j in range(4):
for i in range(4):
if self.map[i][j] == 0: #对数取值不能为0 否则会计算错误
continue
dis_sum += math.log2(self.map[i][j])*wei[i][j] #对数形式
return dis_sum
# 计算分散度，越分散得分越高
def islands(self):
islandsMark = 0
self.marked = [[True]*4]*4
for i in range(4):
for j in range(4):
if self.map[i][j] != 0:
self.marked[i][j] = False
for i in range(4):
for j in range(4):
if self.map[i][j] != 0 and not self.marked[i][j]:
islandsMark += 1
self.mark(i, j, self.map[i][j])
return islandsMark
def mark(self, x, y, value):
# 四个方向向量，方便遍历时使用
vectors = [[0, 1], [1, 0], [-1, 0], [0, -1]]
if x >= 0 and x <= 3 and y >= 0 and y <= 3 and self.map[x][y] != 0 and self.map[x][y] == value and not self.marked[x][y]:
self.marked[x][y] = True
for direction in range(4):
vector = vectors[direction]
self.mark(x+vector[0], y+vector[1], value)
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