Merge branch 'master' into develop-saver-name

ml-agents/mlagents/trainers/tests/test_rl_trainer.py

 from mlagents.trainers.trainer.rl_trainer import RLTrainer
 from mlagents.trainers.tests.test_buffer import construct_fake_buffer
 from mlagents.trainers.agent_processor import AgentManagerQueue
-from mlagents.trainers.settings import TrainerSettings
+from mlagents.trainers.settings import TrainerSettings, FrameworkType
 
 
 # Add concrete implementations of abstract methods
         super()._process_trajectory(trajectory)
 
 
-def create_rl_trainer():
+def create_rl_trainer(framework=FrameworkType.TENSORFLOW):
-        TrainerSettings(max_steps=100, checkpoint_interval=10, summary_freq=20),
+        TrainerSettings(
+            max_steps=100, checkpoint_interval=10, summary_freq=20, framework=framework
+        ),
         True,
         False,
         "mock_model_path",
     assert mocked_save_model.call_count == 0
 
 
+@pytest.mark.parametrize(
+    "framework", [FrameworkType.TENSORFLOW, FrameworkType.PYTORCH], ids=["tf", "torch"]
+)
-def test_summary_checkpoint(mock_add_checkpoint, mock_write_summary):
-    trainer = create_rl_trainer()
+def test_summary_checkpoint(mock_add_checkpoint, mock_write_summary, framework):
+    trainer = create_rl_trainer(framework)
     mock_policy = mock.Mock()
     trainer.add_policy("TestBrain", mock_policy)
     trajectory_queue = AgentManagerQueue("testbrain")
         checkpoint_interval, num_trajectories * time_horizon, checkpoint_interval
     )
     calls = [mock.call(trainer.brain_name, step) for step in checkpoint_range]
+
+    export_ext = "nn" if trainer.framework == FrameworkType.TENSORFLOW else "onnx"
 
     add_checkpoint_calls = [
         mock.call(
-                f"{trainer.model_saver.model_path}/{trainer.brain_name}-{step}.nn",
+                f"{trainer.model_saver.model_path}/{trainer.brain_name}-{step}.{export_ext}",
                 None,
                 mock.ANY,
             ),

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

                 "Trainer has multiple policies, but default behavior only saves the first."
             )
         checkpoint_path = self.model_saver.save_checkpoint(self.brain_name, self.step)
+        export_ext = "nn" if self.framework == FrameworkType.TENSORFLOW else "onnx"
-            f"{checkpoint_path}.nn",
+            f"{checkpoint_path}.{export_ext}",
             self._policy_mean_reward(),
             time.time(),
         )
 
         model_checkpoint = self._checkpoint()
         self.model_saver.copy_final_model(model_checkpoint.file_path)
+        export_ext = "nn" if self.framework == FrameworkType.TENSORFLOW else "onnx"
-            model_checkpoint, file_path=f"{self.model_saver.model_path}.nn"
+            model_checkpoint, file_path=f"{self.model_saver.model_path}.{export_ext}"
         )
         NNCheckpointManager.track_final_checkpoint(self.brain_name, final_checkpoint)