code: def _adjust_penalty(self, observ, old_policy_params, length):
      
      old_policy = self._policy_type(**old_policy_params)
      with tf.name_scope('adjust_penalty'):
        network = self._network(observ, length)
        print_penalty = tf.Print(0, [self._penalty], 'current penalty: ')
        with tf.control_dependencies([print_penalty]):
          kl_change = tf.reduce_mean(self._mask(
              tf.contrib.distributions.kl_divergence(old_policy, network.policy),
              length))
          kl_change = tf.Print(kl_change, [kl_change], 'kl change: ')
          maybe_increase = tf.cond(
              kl_change > 1.3 * self._config.kl_target,
              # pylint: disable=g-long-lambda
              lambda: tf.Print(self._penalty.assign(
                  self._penalty * 1.5), [0], 'increase penalty '),
              float)
          maybe_decrease = tf.cond(
              kl_change < 0.7 * self._config.kl_target,
              # pylint: disable=g-long-lambda
              lambda: tf.Print(self._penalty.assign(
                  self._penalty / 1.5), [0], 'decrease penalty '),
              float)
        with tf.control_dependencies([maybe_increase, maybe_decrease]):
          return tf.summary.merge([
              tf.summary.scalar('kl_change', kl_change),
              tf.summary.scalar('penalty', self._penalty)])
docs: Adjust the KL policy between the behavioral and current policy.
  
      Compute how much the policy actually changed during the multiple
      update steps. Adjust the penalty strength for the next training phase if we
      overshot or undershot the target divergence too much.
  
      Args:
        observ: Sequences of observations.
        old_policy_params: Parameters of the behavioral policy.
        length: Batch of sequence lengths.
  
      Returns:
        Summary tensor.