import tensorflow as tf


    with tf.control_dependencies([baseline_update]):
      self.reward = tf.identity(self.reward)

    self.loss = self.sample_log_prob * (self.reward - self.baseline)
    self.train_step = tf.Variable(0, dtype=tf.int32, trainable=False, name="train_step")

    tf_variables = [var for var in tf.trainable_variables() if var.name.startswith(self.name)]
    print("-" * 80)
    for var in tf_variables:
      print(var)

    self.train_op, self.lr, self.grad_norm, self.optimizer = get_train_ops(
      self.loss,
