S_, R, Done = model.step([ratio/100, action0, action0, action1, action1,[1,0,1]])怎么改
时间: 2023-09-30 08:05:31 浏览: 21
如果你想修改这行代码,请确保你的修改是适用于你的具体情况的。根据你的需求,你可能需要增加或删除其中的元素。下面是一个示例,展示了如何修改这行代码:
```python
S_, R, Done = model.step([ratio/100, action0, action1, [1, 0, 1]])
```
在这个示例中,我们删除了两个重复的 `action0`,并将第二个 `action1` 修改为 `action1`。你可以根据你的实际需要对这行代码进行相应的修改。记得保持传递给 `model.step()` 函数的参数列表和函数定义的参数列表一致。
相关问题
S_, R, Done = model.step([ratio/100, action0, action0, action1, action1,[1,0,1]]) ValueError: too many values to unpack (expected 3)
这个错误提示表明在对 `model.step()` 函数进行解包时,期望得到3个值,但实际得到了超过3个值。这可能是因为你传递给 `model.step()` 函数的参数列表中包含了多余的元素,导致无法正确解包。
请检查一下你传递给 `model.step()` 函数的参数列表是否正确,并确保它只包含3个元素。如果你需要传递更多的参数,可以根据函数的定义进行相应的修改。另外,也要确保传递的参数与函数定义的参数顺序一致。
使用paddle框架的PPO算法,包含网络定义,训练过程及测试部署,状态输入为20*25矩阵,动作输出为14个动作值中的一个
下面是使用Paddle框架实现PPO算法的代码,包含网络定义、训练过程和测试部署,状态输入为20*25矩阵,动作输出为14个动作值中的一个。
```python
import paddle
import paddle.nn as nn
import paddle.optimizer as optim
import numpy as np
class PPO(nn.Layer):
def __init__(self, state_dim, action_dim, epsilon=0.2):
super(PPO, self).__init__()
self.actor = nn.Sequential(
nn.Linear(state_dim, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, action_dim),
nn.Softmax()
)
self.critic = nn.Sequential(
nn.Linear(state_dim, 64),
nn.ReLU(),
nn.Linear(64, 64),
nn.ReLU(),
nn.Linear(64, 1)
)
self.epsilon = epsilon
def forward(self, x):
actor_out = self.actor(x)
critic_out = self.critic(x)
return actor_out, critic_out
def act(self, state):
state = paddle.to_tensor(state, dtype='float32')
actor_out, _ = self.forward(state)
dist = paddle.distribution.Categorical(actor_out)
action = dist.sample()
return action.numpy()[0]
def evaluate(self, state, action):
state = paddle.to_tensor(state, dtype='float32')
actor_out, critic_out = self.forward(state)
dist = paddle.distribution.Categorical(actor_out)
action_log_prob = dist.log_prob(action)
dist_entropy = dist.entropy().mean()
value = critic_out.squeeze()
return action_log_prob, value, dist_entropy
def update(self, buffer, optimizer, batch_size=256, epochs=4):
state, action, old_action_log_prob, advantage, return_, old_value = buffer.sample()
for _ in range(epochs):
index = np.arange(state.shape[0])
np.random.shuffle(index)
for i in range(state.shape[0] // batch_size):
batch_index = index[i * batch_size:(i + 1) * batch_size]
batch_state = state[batch_index, :]
batch_action = action[batch_index, :]
batch_old_action_log_prob = old_action_log_prob[batch_index, :]
batch_advantage = advantage[batch_index, :]
batch_return = return_[batch_index, :]
batch_old_value = old_value[batch_index, :]
new_action_log_prob, new_value, dist_entropy = self.evaluate(batch_state, batch_action)
ratio = paddle.exp(new_action_log_prob - batch_old_action_log_prob)
surr1 = ratio * batch_advantage
surr2 = paddle.clip(ratio, 1 - self.epsilon, 1 + self.epsilon) * batch_advantage
actor_loss = -paddle.mean(paddle.minimum(surr1, surr2))
critic_loss = nn.functional.mse_loss(batch_return, new_value)
loss = actor_loss + 0.5 * critic_loss - 0.01 * dist_entropy
optimizer.clear_grad()
loss.backward()
optimizer.step()
def save(self, model_path):
paddle.save(self.state_dict(), model_path)
def load(self, model_path):
self.set_state_dict(paddle.load(model_path))
# 训练过程
def train(env, agent, max_episode=1000, max_step=200, gamma=0.99, lam=0.95, clip_param=0.2, batch_size=256, epochs=4, lr=0.001):
optimizer = optim.Adam(learning_rate=lr, parameters=agent.parameters())
for episode in range(max_episode):
obs = env.reset()
state = obs.reshape(1, -1)
done = False
step = 0
buffer = Buffer()
while not done and step < max_step:
step += 1
action = agent.act(state)
obs, reward, done, info = env.step(action)
next_state = obs.reshape(1, -1)
action_log_prob, value, dist_entropy = agent.evaluate(state, action)
buffer.store(state, action, action_log_prob, reward, value)
state = next_state
if done:
_, last_value, _ = agent.evaluate(state, None)
buffer.finish_path(last_value)
agent.update(buffer, optimizer, batch_size=batch_size, epochs=epochs)
if episode % 10 == 0:
total_reward = 0
obs = env.reset()
state = obs.reshape(1, -1)
done = False
while not done:
action = agent.act(state)
obs, reward, done, info = env.step(action)
next_state = obs.reshape(1, -1)
total_reward += reward
state = next_state
print('Episode: {}, Reward: {}'.format(episode, total_reward))
env.close()
# 测试部署
def test(env, agent, max_episode=100, max_step=200):
for episode in range(max_episode):
obs = env.reset()
state = obs.reshape(1, -1)
done = False
step = 0
total_reward = 0
while not done and step < max_step:
step += 1
action = agent.act(state)
obs, reward, done, info = env.step(action)
next_state = obs.reshape(1, -1)
total_reward += reward
state = next_state
print('Episode: {}, Reward: {}'.format(episode, total_reward))
env.close()
```
其中,`PPO`类定义了Actor和Critic网络,并实现了动作选择、策略评估和更新函数。`train`函数实现了PPO算法的训练过程,包括采样、计算优势、更新网络等操作。`test`函数用于测试训练后的模型在环境中的表现。