human
Give me a the Python code you requested given the following:
I am working on the file "13. Reinforcement learning.ipynb".
The first task is:
Above is an example of a starting environment in our simple game. The agent controls the blue square, and can move up, down, left, or right. The goal is to move to the green square (for +1 reward) and avoid the red square (for -1 reward). The position of the three blocks is randomized every episode.
Can you write Python code for it?
assistant
#Here is the Python code you requested.

class Qnetwork():
    def __init__(self,h_size):
        #The network recieves a frame from the game, flattened into an array.
        #It then resizes it and processes it through four convolutional layers.
        self.scalarInput =  tf.placeholder(shape=[None,21168],dtype=tf.float32)
        self.imageIn = tf.reshape(self.scalarInput,shape=[-1,84,84,3])
        self.conv1 = slim.conv2d( \
            inputs=self.imageIn,num_outputs=32,kernel_size=[8,8],stride=[4,4],padding='VALID', biases_initializer=None)
        self.conv2 = slim.conv2d( \
            inputs=self.conv1,num_outputs=64,kernel_size=[4,4],stride=[2,2],padding='VALID', biases_initializer=None)
        self.conv3 = slim.conv2d( \
            inputs=self.conv2,num_outputs=64,kernel_size=[3,3],stride=[1,1],padding='VALID', biases_initializer=None)
        self.conv4 = slim.conv2d( \
            inputs=self.conv3,num_outputs=h_size,kernel_size=[7,7],stride=[1,1],padding='VALID', biases_initializer=None)
        
        #We take the output from the final convolutional layer and split it into separate advantage and value streams.
        self.streamAC,self.streamVC = tf.split(self.conv4,2,3)
        self.streamA = slim.flatten(self.streamAC)
        self.streamV = slim.flatten(self.streamVC)
        xavier_init = tf.contrib.layers.xavier_initializer()
        self.AW = tf.Variable(xavier_init([h_size//2,env.actions]))
        self.VW = tf.Variable(xavier_init([h_size//2,1]))
        self.Advantage = tf.matmul(self.streamA,self.AW)
        self.Value = tf.matmul(self.streamV,self.VW)
        
        #Then combine them together to get our final Q-values.
        self.Qout = self.Value + tf.subtract(self.Advantage,tf.reduce_mean(self.Advantage,axis=1,keep_dims=True))
        self.predict = tf.argmax(self.Qout,1)
        
        #Below we obtain the loss by taking the sum of squares difference between the target and prediction Q values.
        self.targetQ = tf.placeholder(shape=[None],dtype=tf.float32)
        self.actions =...