import numpy as np
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)
"direction": direction,
"control": control})
return app
if __name__ == "__main__":
GAME_SIZE = 4
SCORE_TO_WIN = 2048
APP_PORT = 5005
APP_HOST = "localhost"
from game2048.game import Game
game = Game(size=GAME_SIZE, score_to_win=SCORE_TO_WIN)
try:
from game2048.agents import ExpectiMaxAgent,MyAgent
agent = MyAgent(game=game)
except:
from game2048.agents import RandomAgent
print("WARNING: Please compile the ExpectiMaxAgent first following the README.")
print("WARNING: You are now using a RandomAgent.")
agent = RandomAgent(game=game)
print("Run the webapp at http://<any address for your local host>:%s/" % APP_PORT)
app = get_flask_app(game, agent)
app.run(port=APP_PORT, threaded=False, host=APP_HOST) # IMPORTANT: `threaded=False` to ensure correct behavior
if __name__ == '__main__':
GAME_SIZE = 4
SCORE_TO_WIN = 2048
N_TESTS = 50
'''====================
Use your own agent here.'''
from game2048.agents import MyAgent as TestAgent
'''===================='''
size = 4
score_to_win = 2048
# 59*16*12
game = Game(size, score_to_win)
agent = TestAgent(game, display=Display())
scores = []
agent.net7.load_state_dict(
torch.load("net_3_params_7.pkl", map_location='cpu'))
agent.net6.load_state_dict(
torch.load("net_3_params_6.pkl", map_location='cpu'))
agent.net5.load_state_dict(
torch.load("net_3_params_5.pkl", map_location='cpu'))
agent.net4.load_state_dict(
torch.load("net_3_params_4.pkl", map_location='cpu'))
agent.net2.load_state_dict(
torch.load("net_3_params_2.pkl", map_location='cpu'))
step = 0
for i in range(N_TESTS):
from game2048.game import Game
from game2048.displays import Display, IPythonDisplay
from game2048.agents import Agent, RandomAgent, ExpectiMaxAgent, MyAgent
import numpy as np
import pandas as pd
num = 10000
i = 0
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)
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Hyper-parameters
num_classes = 4
if __name__ == '__main__':
# load dataset
dataset_path = '../data/'
dataset_name = dataset_path + 'data_train.csv'
model = ConvNet(num_classes).to(device)
# create model
model_name = 'checkpoint_test.ckpt'
torch.save(model.state_dict(), model_name)
print('save model to {}.'.format(model_name))
# random initialize data set
print('data initalizing ...')
datagen = data.dataprocess.Data(display=None, model_path=model_name)
path = dataset_name
score = 0
# test net structure close to 180(random) is better.
for _ in range(100):
game = Game(size=4, score_to_win=2048)
agent = MyAgent(game, display=None, model_path=model_name)
agent.play()
score += np.sum(game.board)
print("Average total score @50 times is :{:.1f}".format(score / 50))