def build_dqn(num_actions, action_repeat):
 inputs = tf.placeholder(tf.float32, [None, action_repeat, 84, 84])
 net = tf.transpose(inputs, [0, 2, 3, 1])
 net = tflearn.conv_2d(net, 32, 8, strides=4, activation='relu')
 net = tflearn.conv_2d(net, 64, 4, strides=2, activation='relu')
 net = tflearn.fully_connected(net, 256, activation='relu')
 q_values = tflearn.fully_connected(net, num_actions)
 return (inputs, q_values)

def build_dqn ( num_actions , action_repeat ) : inputs = tf . placeholder ( tf . float32 , [ None , action_repeat , 84 , 84 ] ) net = tf . transpose ( inputs , [ 0 , 2 , 3 , 1 ] ) net = tflearn . conv_2d ( net , 32 , 8 , strides = 4 , activation = 'relu' ) net = tflearn . conv_2d ( net , 64 , 4 , strides = 2 , activation = 'relu' ) net = tflearn . fully_connected ( net , 256 , activation = 'relu' ) q_values = tflearn . fully_connected ( net , num_actions ) return ( inputs , q_values )
building a dqn .