Fix comment

ml-agents/mlagents/trainers/sac/trainer.py

     def _is_ready_update(self) -> bool:
         """
         Returns whether or not the trainer has enough elements to run update model
-        :return: A boolean corresponding to whether or not update_model() can be run
+        :return: A boolean corresponding to whether or not _update_policy() can be run
         """
         return (
             self.update_buffer.num_experiences >= self.trainer_parameters["batch_size"]
     @timed
     def _update_policy(self) -> None:
         """
-        If train_interval is met, update the SAC policy given the current reward signals.
-        If reward_signal_train_interval is met, update the reward signals from the buffer.
+        Update the SAC policy and reward signals until the steps_per_update ratio
+        is met.
         """
         self.update_sac_policy()
         self.update_reward_signals()