diff --git a/rltorch/network/NoisyLinear.py b/rltorch/network/NoisyLinear.py
index 066069b..578457b 100644
--- a/rltorch/network/NoisyLinear.py
+++ b/rltorch/network/NoisyLinear.py
@@ -28,17 +28,13 @@ class NoisyLinear(nn.Linear):
   
   def forward(self, x):
     # Fill s_normal_weight with values from the standard normal distribution
-    torch.randn(self.s_normal_weight.size(), out = self.s_normal_weight, 
-                dtype = self.s_normal_weight.dtype, layout = self.s_normal_weight.layout, device = self.s_normal_weight.device)
-    # Multiply by the standard deviation to correct the spread of Gaussian noise
+    self.s_normal_weight.normal_()
     weight_noise = self.sigma_weight * self.s_normal_weight.clone().requires_grad_()
     
     bias = None
     if self.bias is not None:
       # Fill s_normal_bias with values from standard normal
-      torch.randn(self.s_normal_bias.size(), out = self.s_normal_bias, 
-                  dtype = self.s_normal_bias.dtype, layout = self.s_normal_bias.layout, device = self.s_normal_bias.device)
-      # Add guassian noise to original bias
+      self.s_normal_bias.normal_()
       bias = self.bias + self.sigma_bias * self.s_normal_bias.clone().requires_grad_()
     
     return F.linear(x, self.weight + weight_noise, bias)
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