def choose_action(self, state): state = np.array([state]) # 先转成数组再转tensor更高效 state = torch.tensor(state, dtype=torch.float).to(self.device) dist = self.actor(state) value = self.critic(state) action = dist.sample() probs = torch.squeeze(dist.log_prob(action)).item() if self.continuous: action = torch.tanh(action) else: action = torch.squeeze(action).item() value = torch.squeeze(value).item() return action, probs, value
时间: 2024-04-02 17:34:08 浏览: 17
这段代码是一个 PyTorch 实现的 Actor-Critic 算法中的选择动作函数,它接收一个状态 state,并输出一个动作 action、一个概率 probs 和一个价值 value。
具体来说,这个函数首先将 state 转换成一个 numpy 数组,然后将其转换成一个 PyTorch 的 tensor,最后将其移动到指定设备(比如 CPU 或 GPU)上。
接着,函数使用 self.actor 对状态进行前向传播,得到一个分布 dist(比如高斯分布或者离散分布),以及一个值 value(用于评估当前状态的好坏)。
然后,函数从 dist 中采样一个动作 action,并计算出采样该动作的概率 probs(在连续动作空间中,采样后需要进行一定的变换)。
最后,函数将动作 action、概率 probs 和价值 value 返回。
整个过程中,需要注意数据类型的转换和维度的处理,以确保代码的正确性。
相关问题
class Partial_conv3(nn.Module): def __init__(self, dim, n_div, forward): super().__init__() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x 在这段代码中插入全局平均池化做通道增强的模块
可以将全局平均池化放在每个 forward 函数的最后,对每个 channel 进行平均池化操作,然后将结果与未被改变的 channel 拼接起来,得到增强后的输出。修改后的代码如下:
```
class Partial_conv3(nn.Module):
def __init__(self, dim, n_div, forward):
super().__init__()
self.dim_conv3 = dim // n_div
self.dim_untouched = dim - self.dim_conv3
self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False)
self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1)) # 添加全局平均池化层
if forward == 'slicing':
self.forward = self.forward_slicing
elif forward == 'split_cat':
self.forward = self.forward_split_cat
else:
raise NotImplementedError
def forward_slicing(self, x: Tensor) -> Tensor:
# only for inference
x = x.clone() # !!! Keep the original input intact for the residual connection later
x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :])
x[:, :self.dim_conv3, :, :] = self.global_avg_pool(x[:, :self.dim_conv3, :, :]) # 添加全局平均池化
x = torch.cat((x, x[:, self.dim_conv3:, :, :]), 1)
return x
def forward_split_cat(self, x: Tensor) -> Tensor:
x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1)
x1 = self.partial_conv3(x1)
x1 = self.global_avg_pool(x1) # 添加全局平均池化
x = torch.cat((x1, x2), 1)
return x
```
class Partial_conv3(nn.Module): def init(self, dim, n_div, forward): super().init() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_pool = GlobalAvgPool2d() if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.global_pool(x1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x这段代码有什么错误
这段代码并没有明显的语法错误,但是存在一些潜在的问题和需要改进的地方:
1. 类名应该以大写字母开头,应该将 `Partial_conv3` 改为 `PartialConv3`。
2. `super().init()` 应该改为 `super().__init__()`。
3. `self.forward` 方法应该在 `init` 方法中定义,在 `init` 中加入 `self.forward = None`。
4. `forward_split_cat` 方法中,应该先使用 `self.partial_conv3` 对 `x1` 进行卷积操作,再将 `x1` 和 `x2` 进行拼接,即应该将 `x1 = self.partial_conv3(x1)` 放在 `torch.cat((x1, x2), 1)` 之前。
5. `dim` 应该改为 `in_channels`,以更加清晰地表示输入张量的通道数。
6. `forward` 方法的输入参数应该改为 `inputs`,以更加符合命名规范。
以下是修改后的代码:
```
class PartialConv3(nn.Module):
def __init__(self, in_channels, n_div, forward):
super().__init__()
self.dim_conv3 = in_channels // n_div
self.dim_untouched = in_channels - self.dim_conv3
self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False)
self.global_pool = GlobalAvgPool2d()
self.forward = None
if forward == 'slicing':
self.forward = self.forward_slicing
elif forward == 'split_cat':
self.forward = self.forward_split_cat
else:
raise NotImplementedError
def forward_slicing(self, inputs: Tensor) -> Tensor:
# only for inference
x = inputs.clone() # !!! Keep the original input intact for the residual connection later
x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :])
return x
def forward_split_cat(self, inputs: Tensor) -> Tensor:
x1, x2 = torch.split(inputs, [self.dim_conv3, self.dim_untouched], dim=1)
x1 = self.partial_conv3(x1)
x1 = self.global_pool(x1)
x = torch.cat((x1, x2), 1)
return x
```