diff --git a/README.md b/README.md
index 5476837d07889f88af3b746b8d86cf85e11b4fd6..7566023b58290d1b3eb3017c81985635d4be99b2 100644
--- a/README.md
+++ b/README.md
@@ -31,8 +31,24 @@ Here is an example of building an agent with DQN algorithm for atari games.
 import parl
 from parl.algorithms import DQN, DDQN
 
-class CriticModel(parl.Model):
-""" define specific forward model for environment ..."""
+class AtariModel(parl.Model):
+	"""AtariModel
+	This class defines the forward part for an algorithm,
+	its input is state observed on environment.
+	"""
+	def __init__(self, img_shape, action_dim):
+		# define your layers
+		self.cnn1 = layers.conv_2d(num_filters=32, filter_size=5,
+			 			stride=[1, 1], padding=[2, 2], act='relu')
+		...
+		self.fc1 = layers.fc(action_dim)
+	def value(self, img):
+		# define how to estimate the Q value based on the image of atari games.
+		img = img / 255.0
+		l = self.cnn1(img)
+		...
+		Q = self.fc1(l)
+		return Q
 """
 three steps to build an agent
    1.  define a forward model which is critic_model is this example
@@ -41,8 +57,8 @@ three steps to build an agent
    3.  define the I/O part in AtariAgent so that it could update the algorithm based on the interactive data
 """
 
-critic_model = CriticModel(act_dim=2)
-algorithm = DQN(critic_model)
+model = AtariModel(img_shape=(32, 32), action_dim=4)
+algorithm = DQN(model)
 agent = AtariAgent(aglrotihm)
 ```