How to code Actor Critic Reinforcement Learning with continuous action ouput

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) = 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 =
        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 =
      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.