I am working on an Actor-Critic Model with Continuous action output. The output should be any number from 0 to 1. In the Actor part, I am calculating the mu using Sigmoid (to have output from 0 to 1) and variance using ReLU (to always have a positive value).

The code is as follows:

```
class Policy(nn.Module):
"""
implements both actor and critic in one model
"""
def __init__(self):
super(Policy, self).__init__()
self.fc1 = nn.Linear(state_size, 128)
self.fc2 = nn.Linear(128, 64)
# actor's layer
self.action_head = nn.Linear(64, action_size)
self.mu = nn.Sigmoid()
self.var = nn.ReLU()
# critic's layer
self.value_head = nn.Linear(64, 1)
def forward(self, x):
"""
forward of both actor and critic
"""
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
# actor: choses action to take from state s_t
# by returning probability of each action
action_prob = self.action_head(x)
mu = self.mu(action_prob)
var = self.var(action_prob)
# critic: evaluates being in the state s_t
state_values = self.value_head(x)
return mu, var, state_values
class Agent():
def __init__(self, model, is_eval=False, model_name=""):
self.model_name = model_name
self.is_eval = is_eval
self.model = load_model(model_name) if is_eval else model
def act(self, state):
mu, var, state_value = self.model(state)
mu = mu.data.cpu().numpy()
sigma = torch.sqrt(var).data.cpu().numpy()
actions = np.random.normal(mu, sigma)
actions = np.clip(actions, 0, 1) #to have output ranging from 0 to 1
actions = torch.from_numpy(actions)
return actions, state_value
```

Now, lets say action_size = 1.

One output for print(mu, var) is: tensor([0.5266], grad_fn=), tensor([0.7478], grad_fn=)

The action, which is the normal distribution, is 0 (actually it was a negative number before the clipping step).

Now, my question is if the logic is correct to have an Actor-Critic model with Continuous action output.