def data_generator_for_CRNN(score_to_begin, score_to_win, batch_size):
datas = []
labels = []
cnt = 0
while 1:
game = Game(score_to_win = score_to_win, random = False)
agent = ExpectiMaxAgent(game)
while game.end == 0:
step = agent.step()
if game.score >= score_to_begin:
board = board2array(game)
board1 = np.swapaxes(board, 1, 2)
board2 = np.swapaxes(board1, 0, 1).reshape((16, 4, 4, 1))
datas.append(board2)
labels.append(step2array(step))
cnt += 1
game.move(step)
if cnt == batch_size:
cnt = 0
datas = np.array(datas)
labels = np.array(labels)
yield (datas, labels)
datas = []
labels = []
def testAgent(n_tests, game_size, score_to_win, model, max_iter=1000):
acc, total = 0, 0
for i in trange(n_tests):
game, n_iter = Game(game_size, score_to_win), 0
target = ExpectiMaxAgent(game)
while n_iter < max_iter and not target.game.end:
dir_ = Greedy_Action(game, model)
target_dir = target.step()
n_iter += 1
total += 1
if dir_ == target_dir:
acc += 1
target.game.move(dir_)
return acc / total
def data_generator_for_CNN(score_to_begin, score_to_win, batch_size):
datas = []
labels = []
cnt = 0
while 1:
game = Game(score_to_win = score_to_win, random = False)
agent = ExpectiMaxAgent(game)
while game.end == 0:
step = agent.step()
if game.score >= score_to_begin:
datas.append(board2array(game))
labels.append(step2array(step))
cnt += 1
game.move(step)
if cnt == batch_size:
cnt = 0
datas = np.array(datas)
labels = np.array(labels)
yield (datas, labels)
datas = []
labels = []
def data_generator(batch_size):
datas = []
labels = []
cnt = 0
while 1:
game = Game(score_to_win = 2048, random = False)
agent = ExpectiMaxAgent(game)
while game.end == 0:
step = agent.step()
board = game.board / 11
board1 = board.T
datas.append(np.hstack((board, board1)))
labels.append(step2array(step))
cnt += 1
game.move(step)
if cnt == batch_size:
cnt = 0
datas = np.array(datas)
labels = np.array(labels)
yield (datas, labels)
datas = []
labels = []
from game2048.game import Game
from game2048.agents import ExpectiMaxAgent
from game2048.agents import MyAgent
from game2048.displays import Display
import csv
import os
game_size = 4
score_to_win = 2048
iter_num = 3000
game = Game(game_size, score_to_win)
board = game.board
agenta = ExpectiMaxAgent(game, Display())
agentb = MyAgent(game, Display())
directiona = agenta.step()
directionb = agentb.step()
board = game.move(directionb)
i = 0
dic = {}
idx = 0
# save file
filename = '/home/olivia/PycharmProjects/2048/game2048/data/traindata10.csv'
if os.path.exists(filename):
start = True
else:
start = False
os.mknod(filename)
NUM_CLASSES = 4
NUM_EPOCHS = 20
display1 = Display()
display2 = IPythonDisplay()
model = keras.models.load_model('model.h5')
image = []
label = []
for i in range(0, 10):
game = Game(4, score_to_win=2048, random=False)
agent = ExpectiMaxAgent(game, display=display1)
while game.end == False:
direction = agent.step()
image.append(game.board)
label.append(direction)
game.move(direction)
display1.display(game)
#运行10次游戏并记录棋盘和方向
x_train = np.array(image)
y_train = np.array(label)
x_train = np.log2(x_train + 1)
x_train = np.trunc(x_train)
x_train = keras.utils.to_categorical(x_train, 12)
print(x_train.shape)
y_train = keras.utils.to_categorical(y_train, NUM_CLASSES)
game = Game(4, score_to_win=2048, random=False)
agent1 = ExpectiMaxAgent(game, display=display1)
while game.end == False:
if np.sum(game.board) > 384:
break
a = np.array(game.board)
a = np.log2(a + 1)
a = np.trunc(a)
a = keras.utils.to_categorical(a, board_class)
a = a.reshape(1, 4, 4, board_class)
prediction = model.predict(a, batch_size=128)
b = prediction[0]
b = b.tolist()
direction2 = b.index(max(b))
direction1 = agent1.step()
boards.append(game.board)
directions.append(direction1)
game.move(direction2)
display1.display(game)
if np.amax(game.board) == 1024:
count += 1
if count > 98:
break
else:
boards = np.array(boards)
directions = np.array(directions)
x_train, x_test, y_train, y_test = train_test_split(boards,
# for j in range(4):
# #f.write(game.board[i,j])
# print(game.board[i, j], file = f2)
# print(direction, file = f2)
# #f.write(direction)
# game.move(direction)
# #f.write('\n')
for i in range(300):
print("i = ", i)
game = Game(size=GAME_SIZE, score_to_win=SCORE_TO_WIN0)
agent = ExpectiMaxAgent(game=game)
while True:
direction = agent.step()
if (game.end != 0):
break
# print (game.board)
# print ("direction: ", direction)
if game.board.max() < 256:
for i in range(4):
for j in range(4):
#f.write(game.board[i,j])
print(game.board[i, j], file=f1)
print(direction, file=f1)
elif game.board.max() < 512:
for i in range(4):
results = []
direction = []
while i < num:
game = Game(4, score_to_win=2048, random=False)
agent_exp = ExpectiMaxAgent(game)
agent = MyAgent(game)
while (game.score <= 1024) and (not game.end):
A = game.board
A[A == 0] = 1
A = np.log2(A)
A = np.int32(A)
A = A.reshape(16)
dir = agent.step()
# you can change the condition to get different data
if game.score >= 512:
dir_exp = agent_exp.step()
results.append(A)
direction.append(dir_exp)
game.move(dir)
if 0 == i % 100:
# save the result every 100 games
results = np.array(results)
direction = np.array(direction)
final_results = np.c_[results, direction]
final_results = pd.DataFrame(final_results)
final_results.to_csv("data/data_online_1024.csv",
index=False,
header=False,
mode='a+')
results = []
label=[]
display1 = Display()
display2 = Display()
stop_number = 2048
size = int(np.log2(stop_number)) +1 #跑到stop number时所需的one-hot编码位数
for i in range(0,500): #跑500次棋盘，跑到stop_number停止
game = Game(4, score_to_win=2048, random=False)
agent = ExpectiMaxAgent(game, display=display1) #使用强Agent
while game.end==False:
a=np.array(game.board)
direction=agent.step()
image.append(game.board)
label.append(direction)
game.move(direction)
if np.amax(a)==stop_number:
break
display1.display(game)
image=np.array(image) #将得到的数据和标签转换为numpy数组
label=np.array(label)
#划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(image, label, test_size = 0.1, random_state= 30)
def board_log2(board):
for i in range(16):
if board[i] != 0:
board[i] = np.log2(board[i])
return board
# Set the needed number of games
# Limit the max number of steps given by Expectimax each game
NUM_GAMES = 10000
MAX_ITER = 1e3
# This loop takes really a long time
# Use trange to monitor the execution status
for i in trange(NUM_GAMES):
game = Game(size=4, score_to_win=2048)
target = ExpectiMaxAgent(game)
n_iter = 0
data = np.zeros((0, 17), dtype=float)
# To avoid frequent I/O operation,
# data is written into .csv after an entire game rather than one step
while n_iter < MAX_ITER and not target.game.end:
dir_ = target.step()
x = board_log2(np.reshape(target.game.board, newshape=(16, )))
item = np.hstack((x, dir_))
data = np.vstack([data, item])
target.game.move(dir_)
n_iter += 1
df = pd.DataFrame(data, columns=None, index=None)
df.to_csv('./Data_Compressed.csv', mode='a', index=False, header=False)
# print(df)
from game2048.displays import Display, IPythonDisplay
from game2048.agents import Agent, RandomAgent, ExpectiMaxAgent,YourOwnAgent,SimpleNet
import numpy as np
import pandas as pd
display1 = Display()
display2 = IPythonDisplay()
%%time
for i in range(0,3000):
print(i)
game = Game(4, score_to_win=2048, random=False)
#display2.display(game)
agent = ExpectiMaxAgent(game, display=display2)
max_iter=np.inf
n_iter = 0
while (n_iter < max_iter) and (not game.end):
tmp1 = game.board.reshape(1,16)
direction = np.array(agent.step())
tmp3 = direction.reshape(1,1)
a = np.hstack((tmp1,tmp3))
n_iter += 1
agent.game.move(direction)
df1 = pd.DataFrame((a))
df1.to_csv('lastdata.xls',index=0,mode='a',header=0)
model_512 = load_model('myAgent_512.h5')
model_1024 = load_model('myAgent_1024.h5')
boards_256 = []
boards_512 = []
boards_1024 = []
directions_256 = []
directions_512 = []
directions_1024 = []
for i in range(30000):
print('i = ', i)
game = Game(size=4)
expectiMaxAgent = ExpectiMaxAgent(game=game)
while True: # one turn
rightDirection = expectiMaxAgent.step()
if (game.end == True): # game over
break
maxNum = 0
for p in range(4):
for q in range(4):
if game.board[p, q] > maxNum:
maxNum = game.board[p, q]
if maxNum == 2048: # start the next turn
break
inputboard = np.zeros((1, 4, 4, 16))
for p in range(4):
for q in range(4):
num = game.board[p, q]
if num == 0:
inputboard[0, p, q, 0] = 1
