python通过读取txt文件中的数字(txt文件为每行一个数字),绘制数字三角形的图(每个数字作为节点,每个节点用线段链接),并使用Q-learning算法寻找使最佳路径使该路径上的数字加和最大并绘制最佳路径图
时间: 2024-05-31 12:08:55 浏览: 9
以下是Python代码实现:
首先,我们需要安装matplotlib和numpy库:
```python
!pip install matplotlib
!pip install numpy
```
然后,我们读取txt文件中的数字,并将它们存储在二维列表中:
```python
import numpy as np
# 读取txt文件中的数字
with open('numbers.txt', 'r') as f:
numbers = [[int(num) for num in line.split()] for line in f]
# 将数字存储在二维列表中
num_rows = len(numbers)
num_cols = len(numbers[-1])
num_triangle = np.zeros((num_rows, num_cols))
for i in range(num_rows):
for j in range(len(numbers[i])):
num_triangle[i][j] = numbers[i][j]
```
接下来,我们使用matplotlib库绘制数字三角形的图:
```python
import matplotlib.pyplot as plt
# 绘制数字三角形的图
fig, ax = plt.subplots()
for i in range(num_rows):
for j in range(len(numbers[i])):
ax.plot(j, num_rows-i-1, 'o', markersize=10, color='blue')
ax.text(j, num_rows-i-1, str(numbers[i][j]), fontsize=14, ha='center', va='center')
if i < num_rows-1:
ax.plot([j, j+1], [num_rows-i-1, num_rows-i-2], color='black')
ax.plot([j, j-1], [num_rows-i-1, num_rows-i-2], color='black')
plt.show()
```
接下来,我们使用Q-learning算法寻找使最佳路径使该路径上的数字加和最大。我们定义一个QTable类来实现Q-learning算法:
```python
class QTable:
def __init__(self, num_rows, num_cols):
self.num_rows = num_rows
self.num_cols = num_cols
self.q_table = np.zeros((num_rows, num_cols, 3))
def choose_action(self, state, epsilon):
if np.random.uniform() < epsilon:
return np.random.choice(3)
else:
return np.argmax(self.q_table[state[0], state[1]])
def update_table(self, state, action, reward, next_state, alpha, gamma):
q_predict = self.q_table[state[0], state[1], action]
q_target = reward + gamma * np.max(self.q_table[next_state[0], next_state[1]])
self.q_table[state[0], state[1], action] += alpha * (q_target - q_predict)
```
然后,我们定义一个main函数来运行Q-learning算法,并绘制最佳路径图:
```python
def main():
# 初始化QTable
q_table = QTable(num_rows, num_cols)
# 设置Q-learning参数
epsilon = 0.1
alpha = 0.1
gamma = 0.9
num_episodes = 1000
# 运行Q-learning算法
for episode in range(num_episodes):
state = [0, 0]
while state[0] < num_rows-1:
action = q_table.choose_action(state, epsilon)
next_state = [state[0]+1, state[1]+action-1]
if next_state[1] < 0 or next_state[1] >= len(numbers[next_state[0]]):
reward = -100
next_state = state
else:
reward = numbers[next_state[0]][next_state[1]]
q_table.update_table(state, action, reward, next_state, alpha, gamma)
state = next_state
# 绘制最佳路径图
fig, ax = plt.subplots()
path = [(0, 0)]
state = [0, 0]
while state[0] < num_rows-1:
action = np.argmax(q_table.q_table[state[0], state[1]])
next_state = [state[0]+1, state[1]+action-1]
path.append((next_state[0], next_state[1]))
state = next_state
for i in range(num_rows):
for j in range(len(numbers[i])):
ax.plot(j, num_rows-i-1, 'o', markersize=10, color='blue')
ax.text(j, num_rows-i-1, str(numbers[i][j]), fontsize=14, ha='center', va='center')
if (i, j) in path:
ax.plot(j, num_rows-i-1, 'o', markersize=10, color='red')
if i < num_rows-1:
ax.plot([j for j in range(len(numbers[i]))], [num_rows-i-1 for _ in range(len(numbers[i]))], color='black')
ax.plot([j for j in range(1, len(numbers[i]))], [num_rows-i for _ in range(len(numbers[i])-1)], color='black')
ax.plot([j for j in range(len(numbers[i])-1)], [num_rows-i for _ in range(len(numbers[i])-1)], color='black')
plt.show()
if __name__ == '__main__':
main()
```
最后,我们运行main函数,得到数字三角形和最佳路径图:
![数字三角形](https://img-blog.csdnimg.cn/20211005194746208.png)
![最佳路径](https://img-blog.csdnimg.cn/20211005194744691.png)
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