diff --git a/PPO/pytorch/ppo_pytorch.py b/PPO/pytorch/ppo_pytorch.py
index 63970f2..3b253b0 100644
--- a/PPO/pytorch/ppo_pytorch.py
+++ b/PPO/pytorch/ppo_pytorch.py
@@ -18,16 +18,6 @@ import datetime
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")  
 dataType = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor
 
-class Utils():
-    def prepro(self, I):
-        I           = I[35:195] # crop
-        I           = I[::2,::2, 0] # downsample by factor of 2
-        I[I == 144] = 0 # erase background (background type 1)
-        I[I == 109] = 0 # erase background (background type 2)
-        I[I != 0]   = 1 # everything else (paddles, ball) just set to 1
-        X           = I.astype(np.float32).ravel() # Combine items in 1 array 
-        return X
-
 class Actor_Model(nn.Module):
     def __init__(self, state_dim, action_dim):
         super(Actor_Model, self).__init__()   
diff --git a/PPO/tensorflow 2/ppo_tensorflow.py b/PPO/tensorflow 2/ppo_tensorflow.py
index 1852eae..49768d9 100644
--- a/PPO/tensorflow 2/ppo_tensorflow.py	
+++ b/PPO/tensorflow 2/ppo_tensorflow.py	
@@ -11,16 +11,6 @@ import numpy as np
 import sys
 import numpy
 
-class Utils():
-    def prepro(self, I):
-        I           = I[35:195] # crop
-        I           = I[::2,::2, 0] # downsample by factor of 2
-        I[I == 144] = 0 # erase background (background type 1)
-        I[I == 109] = 0 # erase background (background type 2)
-        I[I != 0]   = 1 # everything else (paddles, ball) just set to 1
-        X           = I.astype(np.float32).ravel() # Combine items in 1 array 
-        return X
-
 class Actor_Model(Model):
   def __init__(self, state_dim, action_dim):
     super(Actor_Model, self).__init__()
diff --git a/PPO_continous/tensorflow/ppo_continous_tensorflow.py b/PPO_continous/tensorflow/ppo_continous_tensorflow.py
index 837acfc..8189a6f 100644
--- a/PPO_continous/tensorflow/ppo_continous_tensorflow.py
+++ b/PPO_continous/tensorflow/ppo_continous_tensorflow.py
@@ -11,16 +11,6 @@ import numpy as np
 import sys
 import numpy
 
-class Utils():
-    def prepro(self, I):
-        I           = I[35:195] # crop
-        I           = I[::2,::2, 0] # downsample by factor of 2
-        I[I == 144] = 0 # erase background (background type 1)
-        I[I == 109] = 0 # erase background (background type 2)
-        I[I != 0]   = 1 # everything else (paddles, ball) just set to 1
-        X           = I.astype(np.float32).ravel() # Combine items in 1 array 
-        return X
-
 class Actor_Model(Model):
   def __init__(self, state_dim, action_dim):
     super(Actor_Model, self).__init__()