added team_change as a yaml config

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

     which corresponds to the number of trainer steps between changing learning teams.
     """
 
-    def __init__(self, swap_interval: int, maxlen: int = 10):
+    def __init__(self, maxlen: int = 10):
-        :param swap_interval: Number of trainer steps between changing learning teams.
-        self._swap_interval = swap_interval
-        self._last_swap: Dict[int, int] = {}
-        # Dict from team id to GhostTrainer
+        # Dict from team id to GhostTrainer for ELO calculation
         self._ghost_trainers: Dict[int, GhostTrainer] = {}
 
     def subscribe_team_id(self, team_id: int, trainer: GhostTrainer) -> None:
         """
         if team_id not in self._ghost_trainers:
             self._ghost_trainers[team_id] = trainer
-            self._last_swap[team_id] = 0
-    def get_learning_team(self, step: int) -> int:
+    def get_learning_team(self) -> int:
-        Returns the current learning team. If 'swap_interval' steps have elapsed, the current
-        learning team is added to the end of the queue and then updated with the next in line.
-        :param step: Current step of the trainer.
+        Returns the current learning team.
-        if step >= self._swap_interval + self._last_swap[self._learning_team]:
-            self._last_swap[self._learning_team] = step
-            self._queue.append(self._learning_team)
-            self._learning_team = self._queue.popleft()
-            logger.debug(
-                "Learning team {} swapped on step {}".format(
-                    self._learning_team, self._last_swap
-                )
-            )
+
+    def finish_training(self, step: int) -> None:
+        """
+        The current learning team is added to the end of the queue and then updated with the
+        next in line.
+        :param step: The step of the trainer for debugging
+        """
+        self._queue.append(self._learning_team)
+        self._learning_team = self._queue.popleft()
+        logger.debug(
+            "Learning team {} swapped on step {}".format(self._learning_team, step)
+        )
 
     # Adapted from https://github.com/Unity-Technologies/ml-agents/pull/1975 and
     # https://metinmediamath.wordpress.com/2013/11/27/how-to-calculate-the-elo-rating-including-example/

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

         )
         self.steps_between_save = self_play_parameters.get("save_steps", 20000)
         self.steps_between_swap = self_play_parameters.get("swap_steps", 20000)
+        self.steps_to_train_team = self_play_parameters.get("team_change", 100000)
+
         # Counts the The number of steps of the ghost policies. Snapshot swapping
         # depends on this counter whereas snapshot saving and team switching depends
         # on the wrapped. This ensures that all teams train for the same number of trainer
         self.wrapped_trainer_team: int = None
         self.last_save: int = 0
         self.last_swap: int = 0
+        self.last_team_change: int = 0
 
         # Chosen because it is the initial ELO in Chess
         self.initial_elo: float = self_play_parameters.get("initial_elo", 1200.0)
         self.next_summary_step = self.trainer.next_summary_step
         self.trainer.advance()
 
+        if self.get_step - self.last_team_change > self.steps_to_train_team:
+            self.controller.finish_training()
+            self.last_team_change = self.get_step
+
+        next_learning_team = self.controller.get_learning_team()
+
         # CASE 1: Current learning team is managed by this GhostTrainer.
         # If the learning team changes, the following loop over queues will push the
         # new policy into the policy queue for the new learning agent if
         # pushing fixed snapshots
         # Case 3: No team change. The if statement just continues to push the policy
         # into the correct queue (or not if not learning team).
-        next_learning_team = self.controller.get_learning_team(self.get_step)
         for brain_name in self._internal_policy_queues:
             internal_policy_queue = self._internal_policy_queues[brain_name]
             try: