diff --git a/model.py b/model.py
index 6e36c70..53b0463 100644
--- a/model.py
+++ b/model.py
@@ -11,16 +11,13 @@ LOG_SIG_MIN = -20
 
 
 class ValueNetwork(nn.Module):
-    def __init__(self, state_dim, hidden_dim, init_w=1e-3):
+    def __init__(self, state_dim, hidden_dim):
         super(ValueNetwork, self).__init__()
 
         self.linear1 = nn.Linear(state_dim, hidden_dim)
         self.linear2 = nn.Linear(hidden_dim, hidden_dim)
         self.linear3 = nn.Linear(hidden_dim, 1)
 
-        self.linear3.weight.data.uniform_(-init_w, init_w)
-        self.linear3.bias.data.uniform_(-init_w, init_w)
-
     def forward(self, state):
         x = F.relu(self.linear1(state))
         x = F.relu(self.linear2(x))
@@ -29,16 +26,13 @@ class ValueNetwork(nn.Module):
 
 
 class QNetwork(nn.Module):
-    def __init__(self, num_inputs, num_actions, hidden_size, init_w=1e-3):
+    def __init__(self, num_inputs, num_actions, hidden_size):
         super(QNetwork, self).__init__()
 
         self.linear1 = nn.Linear(num_inputs + num_actions, hidden_size)
         self.linear2 = nn.Linear(hidden_size, hidden_size)
         self.linear3 = nn.Linear(hidden_size, 1)
 
-        self.linear3.weight.data.uniform_(-init_w, init_w)
-        self.linear3.bias.data.uniform_(-init_w, init_w)
-
     def forward(self, state, action):
         x = torch.cat([state, action], 1)
         x = F.relu(self.linear1(x))
@@ -48,19 +42,15 @@ class QNetwork(nn.Module):
 
 
 class GaussianPolicy(nn.Module):
-    def __init__(self, num_inputs, num_actions, hidden_size, init_w=1e-3):
+    def __init__(self, num_inputs, num_actions, hidden_size):
         super(GaussianPolicy, self).__init__()
 
         self.linear1 = nn.Linear(num_inputs, hidden_size)
         self.linear2 = nn.Linear(hidden_size, hidden_size)
 
         self.mean_linear = nn.Linear(hidden_size, num_actions)
-        self.mean_linear.weight.data.uniform_(-init_w, init_w)
-        self.mean_linear.bias.data.uniform_(-init_w, init_w)
 
         self.log_std_linear = nn.Linear(hidden_size, num_actions)
-        self.log_std_linear.weight.data.uniform_(-init_w, init_w)
-        self.log_std_linear.bias.data.uniform_(-init_w, init_w)
 
     def forward(self, state):
         x = F.relu(self.linear1(state))
@@ -79,7 +69,7 @@ class GaussianPolicy(nn.Module):
         normal = Normal(mean, std)
 
         if reparam == True:
-            x_t = normal.rsample() # or mean + std * torch.randn(1,6)
+            x_t = normal.rsample() #mean + std * torch.randn(1,6)
         else:
             x_t = normal.sample()
 
@@ -103,7 +93,6 @@ class GaussianMixturePolicy(nn.Module):
         super(GaussianMixturePolicy, self).__init__()
         self.actions = num_actions
         self.k = k
-        self.log_std_max = LOG_SIG_MAX
 
         self.linear1 = nn.Linear(num_inputs, hidden_size)
         self.linear2 = nn.Linear(hidden_size, hidden_size)