def exploration(board):
#-------------------------------
# 危急情况，使用启发式思考（蒙特卡洛方法）
# 根据危机严重程度分级，确定模拟次数
space = list(board.flatten()).count(0)
n = max(400, (10 - space) * 100)
score_dict = {0: [], 1: [], 2: [], 3: []}
for _ in range(n):
# 初始化随机移动机
rand_game = Game(4, 4096, enable_rewrite_board=True)
rand_game.board = board
# 首次随机移动
first_direction = np.random.randint(0, 4)
rand_game.move_and_score(first_direction)
score = board_score(rand_game.board)
while rand_game.end == 0:
rand_direction = np.random.randint(0, 4)
rand_game.move_and_score(rand_direction)
score_dict[first_direction].append(rand_game.score_move + score)
# print(score_dict)
# 根据均分最高选择移动方向
score_ave = {i: sum(score_dict[i]) / len(score_dict[i]) for i in range(4)}
print(score_ave)
direction = max(score_ave, key=score_ave.get)
max_score = score_ave[direction]
min_direction = min(score_ave, key=score_ave.get)
min_score = score_ave[min_direction]
# 情况十分危急，超级搜索
if max_score < 100 or min_score < 50:
n = 2000
if max_score < 80:
n = 3000
# 存活系数小于50后，每一步都会举步维艰
if max_score < 50:
n = 8000
score_dict = {0: [], 1: [], 2: [], 3: []}
for _ in range(n):
# 初始化随机移动机
rand_game = Game(4, 4096, enable_rewrite_board=True)
rand_game.board = board
# 首次随机移动
first_direction = np.random.randint(0, 4)
rand_game.move_and_score(first_direction)
# score = board_score(rand_game.board)
while rand_game.end == 0:
rand_direction = np.random.randint(0, 4)
rand_game.move_and_score(rand_direction)
score_dict[first_direction].append(rand_game.score_move)
# print(score_dict)
# 根据均分最高选择移动方向
score_ave = {
i: sum(score_dict[i]) / len(score_dict[i])
for i in range(4)
}
print(score_ave)
direction = max(score_ave, key=score_ave.get)
return direction
def generate_fingerprint(AgentClass, **kwargs):
with open("board_cases.json") as f:
board_json = json.load(f)
game = Game(size=4, enable_rewrite_board=True)
agent = AgentClass(game=game, **kwargs)
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.net3.load_state_dict(torch.load("net_3_params_3.pkl", map_location='cpu'))
agent.net2.load_state_dict(
torch.load("net_3_params_2.pkl", map_location='cpu'))
trace = []
for board in board_json:
game.board = np.array(board)
direction = agent.step()
trace.append(direction)
fingerprint = "".join(str(i) for i in trace)
return fingerprint
def step(self):
prev_board = self.game.board
image = [[prev_board[i][j] for j in range(4)] for i in range(4)]
P, Pcount = self.Board8(image)
counter = 0
while True:
counter += 1
select = -1
pmax = -1
for i in range(4):
if pmax < Pcount[i]:
pmax = Pcount[i]
select = i
elif pmax == Pcount[i] and P[select] < P[i]:
pmax = Pcount[i]
select = i
Pcount[select] = -1
new_game = Game(4, enable_rewrite_board=True)
new_game.board = prev_board
new_game.move(3 - select)
new_board = new_game.board
isMoved = not (prev_board == new_board).all()
if isMoved: break
if counter == 4:
select = np.argmax(Pcount)
break
return (3 - select)
def generate_fingerprint(AgentClass, **kwargs):
with open("board_cases.json") as f:
board_json = json.load(f)
game = Game(size=4, enable_rewrite_board=True)
agent = AgentClass(game=game, **kwargs)
trace = []
for board in board_json:
game.board = np.array(board)
direction = agent.step()
trace.append(direction)
fingerprint = "".join(str(i) for i in trace)
return fingerprint
def get_grids_next_step(grid):
#Returns the next 4 states s' from the current state s
grids_list = []
for movement in range(4):
grid_before = grid.copy()
env1 = Game(4, random=False, enable_rewrite_board=True)
env1.board = grid_before
try:
_ = env1.move(movement)
except:
pass
grid_after = env1.board
grids_list.append(grid_after)
return grids_list
def generate_fingerprint(AgentClass, **kwargs):
sess = tf.Session()
with open("board_cases.json") as f:
board_json = json.load(f)
game = Game(size=4, enable_rewrite_board=True)
agent = AgentClass(game=game, sess=sess)
agent.build()
trace = []
num = len(board_json)
for index, board in enumerate(board_json):
print('{} left.'.format(num - index))
game.board = np.array(board)
direction = agent.step()
trace.append(direction)
fingerprint = "".join(str(i) for i in trace)
return fingerprint
def intuition(board):
# ------------------------------
# 正常情况，使用直觉快速思考（近似贪心法）
score_dict = {0: 0, 1: 0, 2: 0, 3: 0}
game = Game(4, 4096, enable_rewrite_board=True)
for i in range(4):
# 每次模拟前置零
game.board = board
game.score_move = 0
game.only_move(i)
# 无合并，就是菜
# print(game.board)
if game.score_move == 0:
score_dict[i] = -100 + board_score(game.board)
# 有合并，计算权值
else:
score_dict[i] += game.score_move + board_score(game.board)
# print('score_dict {}'.format(score_dict))
direction = max(score_dict, key=score_dict.get)
return direction
