[bug-fix] Fix exception thrown when quitting in-editor training from editor (#3885)

ml-agents-envs/mlagents_envs/environment.py

 from mlagents_envs.timers import timed, hierarchical_timer
 from mlagents_envs.exception import (
     UnityEnvironmentException,
-    UnityCommunicationException,
+    UnityCommunicatorStoppedException,
 )
 
 from mlagents_envs.communicator_objects.command_pb2 import STEP, RESET
         if self._loaded:
             outputs = self.communicator.exchange(self._generate_reset_input())
             if outputs is None:
-                raise UnityCommunicationException("Communicator has stopped.")
+                raise UnityCommunicatorStoppedException("Communicator has exited.")
             self._update_behavior_specs(outputs)
             rl_output = outputs.rl_output
             self._update_state(rl_output)
         with hierarchical_timer("communicator.exchange"):
             outputs = self.communicator.exchange(step_input)
         if outputs is None:
-            raise UnityCommunicationException("Communicator has stopped.")
+            raise UnityCommunicatorStoppedException("Communicator has exited.")
         self._update_behavior_specs(outputs)
         rl_output = outputs.rl_output
         self._update_state(rl_output)

ml-agents-envs/mlagents_envs/exception.py

     pass
 
 
+class UnityCommunicatorStoppedException(UnityException):
+    """
+    Raised when communicator has stopped gracefully.
+    """
+
+    pass
+
+
 class UnityObservationException(UnityException):
     """
     Related to errors with receiving observations.

ml-agents/mlagents/trainers/subprocess_env_manager.py

     UnityCommunicationException,
     UnityTimeOutException,
     UnityEnvironmentException,
+    UnityCommunicatorStoppedException,
 )
 from multiprocessing import Process, Pipe, Queue
 from multiprocessing.connection import Connection
         UnityCommunicationException,
         UnityTimeOutException,
         UnityEnvironmentException,
+        UnityCommunicatorStoppedException,
     ) as ex:
         logger.info(f"UnityEnvironment worker {worker_id}: environment stopping.")
         step_queue.put(

ml-agents/mlagents/trainers/trainer_controller.py

 from mlagents_envs.exception import (
     UnityEnvironmentException,
     UnityCommunicationException,
+    UnityCommunicatorStoppedException,
 )
 from mlagents.trainers.sampler_class import SamplerManager
 from mlagents_envs.timers import hierarchical_timer, timed
             KeyboardInterrupt,
             UnityCommunicationException,
             UnityEnvironmentException,
+            UnityCommunicatorStoppedException,
-            if isinstance(ex, KeyboardInterrupt):
+            if isinstance(ex, KeyboardInterrupt) or isinstance(
+                ex, UnityCommunicatorStoppedException
+            ):
-        if self.train_model:
-            self._export_graph()
+        finally:
+            if self.train_model:
+                self._export_graph()
 
     def end_trainer_episodes(
         self, env: EnvManager, lessons_incremented: Dict[str, bool]