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import numpy as np
import random
class Game2048:
def __init__(self, size=4):
self.size = size
self.reset()
def reset(self):
"""重置游戏状态"""
self.board = np.zeros((self.size, self.size), dtype=np.int32)
self.score = 0
self.add_tile()
self.add_tile()
self.game_over = False
return self.board.copy()
def add_tile(self):
"""在随机空位置添加新方块(90%概率为2,10%概率为4)"""
empty_cells = []
for i in range(self.size):
for j in range(self.size):
if self.board[i][j] == 0:
empty_cells.append((i, j))
if empty_cells:
i, j = random.choice(empty_cells)
self.board[i][j] = 2 if random.random() < 0.9 else 4
def move(self, direction):
"""
执行移动操作
0: 上, 1: 右, 2: 下, 3: 左
返回: (新棋盘状态, 游戏是否结束)
"""
moved = False
# 根据方向执行移动
if direction == 0: # 上
for j in range(self.size):
column = self.board[:, j].copy()
new_column, moved_col = self.slide(column)
if moved_col:
moved = True
self.board[:, j] = new_column
elif direction == 1: # 右
for i in range(self.size):
row = self.board[i, :].copy()[::-1]
new_row, moved_row = self.slide(row)
if moved_row:
moved = True
self.board[i, :] = new_row[::-1]
elif direction == 2: # 下
for j in range(self.size):
column = self.board[::-1, j].copy()
new_column, moved_col = self.slide(column)
if moved_col:
moved = True
self.board[:, j] = new_column[::-1]
elif direction == 3: # 左
for i in range(self.size):
row = self.board[i, :].copy()
new_row, moved_row = self.slide(row)
if moved_row:
moved = True
self.board[i, :] = new_row
# 如果发生了移动，添加新方块并检查游戏结束
if moved:
self.add_tile()
self.check_game_over()
return self.board.copy(), self.game_over
def slide(self, line):
"""处理单行/列的移动和合并逻辑"""
non_zero = line[line != 0]
new_line = np.zeros_like(line)
idx = 0
score_inc = 0
moved = False
# 检查是否移动
if not np.array_equal(non_zero, line[:len(non_zero)]):
moved = True
# 合并相同数字
i = 0
while i < len(non_zero):
if i + 1 < len(non_zero) and non_zero[i] == non_zero[i+1]:
new_val = non_zero[i] * 2
new_line[idx] = new_val
score_inc += new_val
i += 2
idx += 1
else:
new_line[idx] = non_zero[i]
i += 1
idx += 1
self.score += score_inc
return new_line, moved or (score_inc > 0)
def check_game_over(self):
"""检查游戏是否结束"""
# 检查是否还有空格子
if np.any(self.board == 0):
self.game_over = False
return
# 检查水平和垂直方向是否有可合并的方块
for i in range(self.size):
for j in range(self.size - 1):
if self.board[i][j] == self.board[i][j+1]:
self.game_over = False
return
for j in range(self.size):
for i in range(self.size - 1):
if self.board[i][j] == self.board[i+1][j]:
self.game_over = False
return
self.game_over = True
def get_valid_moves(self):
"""获取当前所有有效移动方向"""
valid_moves = []
# 检查上移是否有效
for j in range(self.size):
column = self.board[:, j].copy()
new_column, _ = self.slide(column)
if not np.array_equal(new_column, self.board[:, j]):
valid_moves.append(0)
break
# 检查右移是否有效
for i in range(self.size):
row = self.board[i, :].copy()[::-1]
new_row, _ = self.slide(row)
if not np.array_equal(new_row[::-1], self.board[i, :]):
valid_moves.append(1)
break
# 检查下移是否有效
for j in range(self.size):
column = self.board[::-1, j].copy()
new_column, _ = self.slide(column)
if not np.array_equal(new_column[::-1], self.board[:, j]):
valid_moves.append(2)
break
# 检查左移是否有效
for i in range(self.size):
row = self.board[i, :].copy()
new_row, _ = self.slide(row)
if not np.array_equal(new_row, self.board[i, :]):
valid_moves.append(3)
break
return valid_moves
def get_state(self):
"""获取当前游戏状态表示（用于AI模型）"""
# 创建4个通道的状态表示
state = np.zeros((4, self.size, self.size), dtype=np.float32)
# 通道0: 当前方块值的对数（归一化）
for i in range(self.size):
for j in range(self.size):
if self.board[i][j] > 0:
state[0, i, j] = np.log2(self.board[i][j]) / 16.0 # 支持到65536 (2^16)
# 通道1: 空格子指示器
state[1] = (self.board == 0).astype(np.float32)
# 通道2: 可合并的邻居指示器
for i in range(self.size):
for j in range(self.size):
if self.board[i][j] > 0:
# 检查右侧
if j < self.size - 1 and self.board[i][j] == self.board[i][j+1]:
state[2, i, j] = 1.0
state[2, i, j+1] = 1.0
# 检查下方
if i < self.size - 1 and self.board[i][j] == self.board[i+1][j]:
state[2, i, j] = 1.0
state[2, i+1, j] = 1.0
# 通道3: 最大值位置（归一化）
max_value = np.max(self.board)
if max_value > 0:
max_positions = np.argwhere(self.board == max_value)
for pos in max_positions:
state[3, pos[0], pos[1]] = 1.0
return state |