From 5be4ca0029e1e092d19905eff734aa996489c1ee Mon Sep 17 00:00:00 2001
From: Bo Zhou <zhoubo01@baidu.com>
Date: Tue, 4 Dec 2018 21:11:27 +0800
Subject: [PATCH] Update README.md (#34)

a more detailed example for DQN model.
---
 README.md | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 5476837..7566023 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)
 ```
 
-- 
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