def default_policy(state):
#需要一种新的衡量reward的方式
game = Game()
game.setstate(state)
depth = 0
while (depth < 15 and not game.end):
actions = getactions(game.getstate())
a = random.choice(actions)
game.move(a)
depth = depth + 1
return game.getscore()
def MCTStreesearch(state):
max_depth = 10
max_iter = 60
branch_scores = [0] * 4
branch_counts = [0] * 4
available_branches = []
for i in range(4):
if (just_move(state.copy(), i) == 1):
available_branches.append(i)
for i in range(max_iter):
branch = choice(available_branches) #随机选取一个方向
game = Game()
game.setstate(state)
if (game.end):
return 4
depth = 0
while (True):
if game.end or depth > max_depth:
branch_scores[branch] += game.getscore()
branch_counts[branch] += 1
break
# first move is down the selected branch
if depth == 0:
next_move = branch
else:
# otherwise play out randomly
available_moves = []
current_state = game.getstate()
for i in range(4):
if (just_move(current_state.copy(), i) == 1):
available_moves.append(i)
next_move = choice(available_moves)
# keep track of score based on move selection
game.move(next_move)
depth += 1
branch_counts = np.array(branch_counts)
branch_counts = np.where(branch_counts == 0, 1.0,
branch_counts) # avoid divide by zero
branch_results = np.array(branch_scores) / branch_counts
move = np.where(branch_results == np.max(branch_results))[0][0]
return move
from my2048 import Game
from MCTStree import MCTStreesearch
import numpy as np
'''
输入是16个int型的数据，用分号隔开，表示棋盘格局
输出是0~4的数据
0 1 2 3表示的是上，右，下，左
如果输出4 则表示已经死局
java只负责发送棋盘数据，python负责处理AI逻辑，分数与随机数生成都由java负责
'''
if __name__ == "__main__":
letjit = np.array([[0, 0, 0, 0], [0, 2, 2, 0], [0, 2, 2, 0], [0, 0, 0, 0]],
dtype='int32')
MCTStreesearch(letjit) #run the function once so that it auto jit
game = Game()
while (True):
message = input()
if (message == "end"):
break
game.build(message)
state = game.getstate()
print(MCTStreesearch(state))
import matplotlib.pyplot as plt
import matplotlib
from my2048 import Game
from MCTStree import MCTStreesearch
from uct import uct_search
# 设置中文字体和负号正常显示
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
matplotlib.rcParams['axes.unicode_minus'] = False
score = []
maxtile = []
index = [i for i in range(10)]
for i in range(10):
game = Game()
while (not game.end):
state = game.getstate()
#move = MCTStreesearch(state)
move = uct_search(state, 60)
game.move(move)
print(game.info())
score.append(game.getscore())
maxtile.append(game.max())
plt.xlabel('iter')
plt.ylabel('num')
plt.plot(index, score, color='skyblue', label='游戏分数')
plt.plot(index, maxtile, color='green', label='最大方块值')
plt.legend()
plt.show()
def next_state(state, a):
game = Game()
game.setstate(state)
game.move(a)
return game.getstate()
def is_terminal(self):
game = Game()
game.setstate(self.state)
return game.end
next_move = branch
else:
# otherwise play out randomly
available_moves = []
current_state = game.getstate()
for i in range(4):
if (just_move(current_state.copy(), i) == 1):
available_moves.append(i)
next_move = choice(available_moves)
# keep track of score based on move selection
game.move(next_move)
depth += 1
branch_counts = np.array(branch_counts)
branch_counts = np.where(branch_counts == 0, 1.0,
branch_counts) # avoid divide by zero
branch_results = np.array(branch_scores) / branch_counts
move = np.where(branch_results == np.max(branch_results))[0][0]
return move
if __name__ == "__main__":
t1 = time.time()
game = Game()
while (not game.end):
state = game.getstate()
move = MCTStreesearch(state)
game.move(move)
print(game.info())
t2 = time.time()
print(t2 - t1)
import torch
from section_cnn import CNNformuti
from my2048 import Game
datalist = [
2**x for x in range(16)
] #[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768]
datalist[0] = 0
'''
这是cnn的改进版，采用了多个卷积核滤过，然后cat在一起，进入全连接层，可以学习到更多的特征
从单通道变为了16通道，值映射到了0~1之间，更适合训练，准确率能到达70左右，但是实操的时候还是不尽人意
问题在于，生成的数据可能真的不太适合训练，他看不太出其中的规律，然后会出现无效的移动，死循环在本地
'''
state_dict = torch.load("pkl/mutichannelCNN_parameter.pkl", map_location='cpu')
game = Game()
net = CNNformuti()
net.load_state_dict(state_dict)
steps = 0
while not game.end:
game.display()
grid = game.getstate()
muti_data = np.zeros(shape=(16, 4, 4), dtype='float32')
for i in range(4):
for j in range(4):
v = grid[i, j]
muti_data[datalist.index(v), i, j] = 1.0
grid = torch.from_numpy(muti_data.reshape((1, 16, 4, 4)))
output = net(grid)
index = torch.argmax(output)
from my2048 import Game, print_grid
from minimax import search_minimax
import numpy as np
game = Game()
while not game.end:
state = game.getstate()
print_grid(state)
bestmove = search_minimax(state)
print("AI suggests bestmove is:" + str(bestmove))
game.move(bestmove)
message = game.info()
print(message)
# game.setstate(np.array([[2,2,8,16],
# [4,4,16,32],
# [2,2,8,64],
# [2,4,2,512]],dtype='int32'))
# state = game.getstate()
# print_grid(state)
# bestmove = search_minimax(state)
# print("AI suggests bestmove is:" + str(bestmove))
# game.move(bestmove)