-

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

 logger = get_logger(__name__)
 
 
+from pympler import muppy, summary
+
+
 class RLTrainer(Trainer):  # pylint: disable=abstract-method
     """
     This class is the base class for trainers that use Reward Signals.
             self._next_summary_step = self._get_next_interval_step(self.summary_freq)
         if step_after_process >= self._next_summary_step and self.get_step != 0:
             self._write_summary(self._next_summary_step)
-            from guppy import hpy; h = hpy(); print(h.heap())
+            all_objects = muppy.get_objects()
+            sum1 = summary.summarize(all_objects)
+            summary.print_(sum1)
 
     def _maybe_save_model(self, step_after_process: int) -> None:
         """
                 self.trainer_settings.checkpoint_interval
             )
         if step_after_process >= self._next_save_step and self.get_step != 0:
-            from guppy import hpy; h = hpy(); print(h.heap())
             self._checkpoint()
 
     def advance(self) -> None: