能写一个玩贪吃蛇的卷积神经模型吗
时间: 2024-01-06 13:03:33 浏览: 33
当然可以!以下是一个使用卷积神经网络(CNN)来玩贪吃蛇的示例代码:
```python
import numpy as np
import random
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten
# 游戏窗口的大小和贪吃蛇网格的大小
WINDOW_WIDTH = 20
WINDOW_HEIGHT = 20
GRID_SIZE = 20
# 初始化游戏状态
def init_game_state():
return [WINDOW_WIDTH // 2, WINDOW_HEIGHT // 2], generate_food(), False
# 随机生成食物的位置
def generate_food():
return [random.randint(0, WINDOW_WIDTH - 1), random.randint(0, WINDOW_HEIGHT - 1)]
# 获取当前游戏状态的输入
def get_input(game_state):
snake_head, food, _ = game_state
input_grid = np.zeros((WINDOW_WIDTH, WINDOW_HEIGHT))
input_grid[snake_head[0]][snake_head[1]] = 1 # 蛇头位置设为1
input_grid[food[0]][food[1]] = -1 # 食物位置设为-1
return input_grid.reshape(1, WINDOW_WIDTH, WINDOW_HEIGHT, 1)
# 判断游戏是否结束
def is_game_over(game_state):
snake_head, _, game_over = game_state
if snake_head[0] < 0 or snake_head[0] >= WINDOW_WIDTH or snake_head[1] < 0 or snake_head[1] >= WINDOW_HEIGHT:
return True # 碰到墙壁,游戏结束
return game_over
# 执行动作
def perform_action(game_state, action):
snake_head, food, _ = game_state
if action == 0: # 上
snake_head[1] -= 1
elif action == 1: # 下
snake_head[1] += 1
elif action == 2: # 左
snake_head[0] -= 1
elif action == 3: # 右
snake_head[0] += 1
return snake_head, food, False
# 创建卷积神经网络模型
def create_model():
model = Sequential()
model.add(Conv2D(16, kernel_size=(3, 3), activation='relu', input_shape=(WINDOW_WIDTH, WINDOW_HEIGHT, 1)))
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(4, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
return model
# 训练模型
def train(model, X_train, y_train):
model.fit(X_train, y_train, epochs=10)
# 使用模型进行预测
def predict(model, X):
return np.argmax(model.predict(X))
# 游戏主循环
def game_loop(model):
game_state = init_game_state()
while not is_game_over(game_state):
input_grid = get_input(game_state)
action = predict(model, input_grid)
game_state = perform_action(game_state, action)
return
# 训练数据集大小
TRAIN_SIZE = 10000
# 创建训练数据集
X_train = []
y_train = []
for _ in range(TRAIN_SIZE):
game_state = init_game_state()
while not is_game_over(game_state):
input_grid = get_input(game_state)
action = random.randint(0, 3) # 随机选择动作
X_train.append(input_grid)
y = np.zeros(4)
y[action] = 1
y_train.append(y)
game_state = perform_action(game_state, action)
X_train = np.array(X_train)
y_train = np.array(y_train)
# 创建并训练模型
model = create_model()
train(model, X_train, y_train)
# 进行游戏
game_loop(model)
```
这个示例代码使用卷积神经网络(CNN)来训练一个贪吃蛇智能体,并在游戏循环中使用该模型进行预测来决定下一步的动作。训练数据集通过随机生成的游戏状态和随机选择的动作来创建。模型训练使用了Keras库。
请注意,这只是一个简单的示例,可能需要进行更多的优化和改进,以便使智能体在贪吃蛇游戏中表现更好。希望对你有帮助!
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