用python写一个简单的reinforce模型
时间: 2024-05-15 13:15:48 浏览: 20
以下是一个简单的reinforce模型的Python实现,用于解决CartPole问题:
```python
import numpy as np
import gym
env = gym.make('CartPole-v0')
# 神经网络参数
input_size = env.observation_space.shape[0]
hidden_size = 16
output_size = env.action_space.n
# 初始化权重
W1 = np.random.randn(input_size, hidden_size) / np.sqrt(input_size)
b1 = np.zeros(hidden_size)
W2 = np.random.randn(hidden_size, output_size) / np.sqrt(hidden_size)
b2 = np.zeros(output_size)
# 学习率
learning_rate = 0.01
# 训练次数
num_episodes = 1000
# 每个episode的最大步数
max_steps = 200
# 记录每个episode的奖励
reward_list = []
# 训练模型
for i in range(num_episodes):
observation = env.reset()
episode_reward = 0
for j in range(max_steps):
# 前向传播
h = np.dot(observation, W1) + b1
h[h < 0] = 0
logits = np.dot(h, W2) + b2
probs = np.exp(logits) / np.sum(np.exp(logits))
# 选择动作
action = np.random.choice(output_size, p=probs)
observation, reward, done, _ = env.step(action)
episode_reward += reward
# 计算梯度
dlogits = probs
dlogits[action] -= 1
dW2 = np.outer(h, dlogits)
db2 = dlogits
dh = np.dot(dlogits, W2.T)
dh[h <= 0] = 0
dW1 = np.outer(observation, dh)
db1 = dh
# 更新权重
W2 -= learning_rate * dW2
b2 -= learning_rate * db2
W1 -= learning_rate * dW1
b1 -= learning_rate * db1
if done:
break
reward_list.append(episode_reward)
print('Episode %d, Reward: %d' % (i, episode_reward))
# 测试模型
observation = env.reset()
for i in range(max_steps):
env.render()
h = np.dot(observation, W1) + b1
h[h < 0] = 0
logits = np.dot(h, W2) + b2
probs = np.exp(logits) / np.sum(np.exp(logits))
action = np.argmax(probs)
observation, reward, done, _ = env.step(action)
if done:
break
env.close()
```
在训练过程中,我们使用前向传播来计算动作的概率,然后使用概率来选择动作。然后我们计算梯度并更新权重。在测试过程中,我们使用前向传播来选择最可能的动作并渲染环境。