Remove empty_queue interface

ml-agents/mlagents/trainers/agent_processor.py

         """
         return self._behavior_id
 
+    def qsize(self) -> int:
+        """
+        Returns the approximate size of the queue. Note that values may differ
+        depending on the underlying queue implementation.
+        """
+        return self._queue.qsize()
+
     def empty(self) -> bool:
         return self._queue.empty()
 

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

             self.change_current_elo(change)
             self._stats_reporter.add_stat("Self-play/ELO", self.current_elo)
 
-    def advance(self, empty_queue: bool = False) -> None:
+    def advance(self) -> None:
         """
         Steps the trainer, passing trajectories to wrapped trainer and calling trainer advance
         """
                     # We grab at most the maximum length of the queue.
                     # This ensures that even if the queue is being filled faster than it is
                     # being emptied, the trajectories in the queue are on-policy.
-                    for _ in range(trajectory_queue.maxlen):
-                        t = trajectory_queue.get(block=not empty_queue, timeout=0.05)
+                    for _ in range(trajectory_queue.qsize()):
+                        t = trajectory_queue.get(block=False)
-                        if not empty_queue:
-                            break
-                    for _ in range(trajectory_queue.maxlen):
-                        t = trajectory_queue.get(block=not empty_queue, timeout=0.05)
+                    for _ in range(trajectory_queue.qsize()):
+                        t = trajectory_queue.get(block=False)
-                        if not empty_queue:
-                            break
-        self.trainer.advance(empty_queue=empty_queue)
+        self.trainer.advance()
         if self.get_step - self.last_team_change > self.steps_to_train_team:
             self.controller.change_training_team(self.get_step)
             self.last_team_change = self.get_step

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

         if step_after_process >= self.next_summary_step and self.get_step != 0:
             self._write_summary(self.next_summary_step)
 
-    def advance(self, empty_queue: bool = False) -> None:
+    def advance(self) -> None:
-        :param empty_queue: Whether or not to empty the queue when called. For synchronous
-            operation, we need to do so to avoid the queue filling up.
         """
         with hierarchical_timer("process_trajectory"):
             for traj_queue in self.trajectory_queues:
-                for _ in range(traj_queue.maxlen):
+                for _ in range(traj_queue.qsize()):
-                        t = traj_queue.get(block=not empty_queue, timeout=0.05)
+                        t = traj_queue.get(block=False)
-                        if not empty_queue:
-                            break
                     except AgentManagerQueue.Empty:
                         break
         if self.should_still_train:

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

         pass
 
     @abc.abstractmethod
-    def advance(self, empty_queue: bool = False) -> None:
+    def advance(self) -> None:
-        :param empty_queue: Whether or not to empty the queue when called. For synchronous
-            operation, we need to do so to avoid the queue filling up.
         """
         pass
 

ml-agents/mlagents/trainers/trainer_controller.py

         if not self.threaded:
             with hierarchical_timer("trainer_advance"):
                 for trainer in self.trainers.values():
-                    trainer.advance(empty_queue=True)
+                    trainer.advance()
 
         return num_steps