import tensorflow.contrib.layers as layers

def simple_model(img_in, num_actions, scope, reuse=False, num_filters=64):
    with tf.variable_scope(scope, reuse=reuse):
        out = img_in
        gauss_initializer = initializers.xavier_initializer(uniform=False)  # stddev = 1/n
        with tf.variable_scope("convnet"):
            out = layers.convolution2d(
                out, num_outputs=num_filters, kernel_size=8, stride=4,
                activation_fn=tf.nn.relu, weights_initializer=gauss_initializer,
                trainable=False)
        out = layers.flatten(out)
