I have a neural network that is supposed to train an agent to do a task, but when I run the model, the weight on the second fully connected layer (fc2) on the Critic becomes very small.
> tensor([[ 4.1137e-41,  2.2757e-42, -2.6926e-41,  ..., -2.0870e-41,
>          -6.6328e-41,  2.3732e-06],
>         [-2.2255e-41,  5.6645e-41,  1.4829e-41,  ..., -1.2250e-41,
>           1.5626e-41,  4.0754e-04],
>         [ 4.9889e-41,  1.1880e-41, -3.1507e-41,  ...,  5.8521e-41,
>           5.0874e-41,  4.2433e-06],
>         ...,
>         [-5.5420e-41,  3.9210e-41,  3.0439e-41,  ...,  2.9665e-42,
>          -3.1451e-41, -1.0182e-04],
>         [-5.7013e-41, -1.1474e-41, -4.4497e-41,  ...,  9.9604e-42,
>          -3.4307e-41, -4.3556e-07],
>         [ 2.5703e-41,  5.2402e-41, -6.8396e-41,  ...,  2.6696e-41,
>           4.3401e-41, -4.7599e-09]], requires_grad=True)

This causes my agent to grab the upper limits on my action space and stay there. I thought nn.LayerNorm will help solve this problem, but I having no luck.
Here is my code.
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np


class CriticNetwork(nn.Module):
    def __init__(self, beta, input_dims, fc1_dims, fc2_dims, n_actions, name,
                 chkpt_dir='tmp/ddpg'):
        super(CriticNetwork, self).__init__()
        self.input_dims = input_dims
        self.fc1_dims = fc1_dims
        self.fc2_dims = fc2_dims
        self.n_actions = n_actions
        self.name = name
        self.checkpoint_dir = chkpt_dir
        self.checkpoint_file = os.path.join(self.checkpoint_dir, name + '_ddpg')

        self.fc1 = nn.Linear(*self.input_dims, self.fc1_dims)
        self.fc2 = nn.Linear(self.fc1_dims, self.fc2_dims)

        self.bn1 = nn.LayerNorm(self.fc1_dims)
        self.bn2 = nn.LayerNorm(self.fc2_dims)

        self.action_value = nn.Linear(self.n_actions, self.fc2_dims)

        self.q = nn.Linear(self.fc2_dims, 1)

        f1 = 1./np.sqrt(self.fc1.weight.data.size()[0])
        self.fc1.weight.data.uniform_(-f1, f1)
        self.fc1.bias.data.uniform_(-f1, f1)

        f2 = 1./np.sqrt(self.fc2.weight.data.size()[0])
        self.fc2.weight.data.uniform_(-f2, f2)
        self.fc2.bias.data.uniform_(-f2, f2)

        f3 = 0.003
        self.q.weight.data.uniform_(-f3, f3)
        self.q.bias.data.uniform_(-f3, f3)
        #print(self.q.weight.data)

        f4 = 1./np.sqrt(self.action_value.weight.data.size()[0])
        self.action_value.weight.data.uniform_(-f4, f4)
        self.action_value.bias.data.uniform_(-f4, f4)

        self.optimizer = optim.Adam(self.parameters(), lr=beta, weight_decay=0.01)
        self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
        self.to(self.device)

    def forward(self, state, action):
        state_value = self.fc1(state)
        state_value = self.bn1(state_value)
        state_value = F.relu(state_value)
        state_value = self.fc2(state_value)
        state_value = self.bn2(state_value)
        action_value = self.action_value(action)
        state_action_value = F.relu(torch.add(state_value, action_value))
        state_action_value = self.q(state_action_value)

        return state_action_value

    def save_checkpoint(self):
        print('... saving checkpoint ...')
        torch.save(self.state_dict(), self.checkpoint_file)

    def load_checkpoint(self):
        print(' ... loading checkpoint ... ')
        self.load_state_dict(torch.load(self.checkpoint_file))

    #def troubleshoot(self):
        #print('')


class ActorNetwork(nn.Module):
    def __init__(self, alpha, input_dims, fc1_dims, fc2_dims, n_actions, name,
                 chkpt_dir='tmp/ddpg'):
        super(ActorNetwork, self).__init__()
        self.input_dims = input_dims
        self.fc1_dims = fc1_dims
        self.fc2_dims = fc2_dims
        self.n_actions = n_actions
        self.name = name
        self.checkpoint_dir = chkpt_dir
        self.checkpoint_file = os.path.join(self.checkpoint_dir, name + '_ddpg')

        self.fc1 = nn.Linear(*self.input_dims, self.fc1_dims)
        self.fc2 = nn.Linear(self.fc1_dims, self.fc2_dims)

        self.bn1 = nn.LayerNorm(self.fc1_dims)
        self.bn2 = nn.LayerNorm(self.fc2_dims)

        self.mu = nn.Linear(self.fc2_dims, self.n_actions)

        f1 = 1./np.sqrt(self.fc1.weight.data.size()[0])
        self.fc1.weight.data.uniform_(-f1, f1)
        self.fc1.bias.data.uniform_(-f1, f1)

        f2 = 1./np.sqrt(self.fc2.weight.data.size()[0])
        self.fc2.weight.data.uniform_(-f2, f2)
        self.fc2.bias.data.uniform_(-f2, f2)

        f3 = 0.003
        self.mu.weight.data.uniform_(-f3, f3)
        self.mu.bias.data.uniform_(-f3, f3)

        self.optimizer = optim.Adam(self.parameters(), lr=alpha)
        self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
        self.to(self.device)

    def forward(self, state):
        x = self.fc1(state)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = self.bn2(x)
        x = F.relu(x)
        x = torch.tanh(self.mu(x))

        return x

    def save_checkpoint(self):
        print('... saving checkpoint ...')
        torch.save(self.state_dict(), self.checkpoint_file)

    def load_checkpoint(self):
        print(' ... loading checkpoint ... ')
        self.load_state_dict(torch.load(self.checkpoint_file))

I need help preventing the critic network weights from becoming small (vanishing gradient). Thanks!