[Tune] Add export_formats option to export policy graphs (#3868)

In earlier PRs, PR#3585 and PR#3637, export_policy_model and export_policy_checkpoint were introduced for users to export TensorFlow model and checkpoint.

For Ray Tune users, these APIs are not accessible through YAML configurations.

In this pull request, export_formats option is provided to enable users to choose the desired export format.

python/ray/tune/trainable.py

@@ -331,6 +331,23 @@ class Trainable(object):
         self.restore(checkpoint_path)
         shutil.rmtree(tmpdir)
 
+    def export_model(self, export_formats, export_dir=None):
+        """Exports model based on export_formats.
+
+        Subclasses should override _export_model() to actually
+        export model to local directory.
+
+        Args:
+            export_formats (list): List of formats that should be exported.
+            export_dir (str): Optional dir to place the exported model.
+                Defaults to self.logdir.
+
+        Return:
+            A dict that maps ExportFormats to successfully exported models.
+        """
+        export_dir = export_dir or self.logdir
+        return self._export_model(export_formats, export_dir)
+
     def reset_config(self, new_config):
         """Resets configuration without restarting the trial.
 
@@ -402,6 +419,18 @@ class Trainable(object):
         """Subclasses should override this for any cleanup on stop."""
         pass
 
+    def _export_model(self, export_formats, export_dir):
+        """Subclasses should override this to export model.
+
+        Args:
+            export_formats (list): List of formats that should be exported.
+            export_dir (str): Directory to place exported models.
+
+        Return:
+            A dict that maps ExportFormats to successfully exported models.
+        """
+        return {}
+
 
 def wrap_function(train_func):
     from ray.tune.function_runner import FunctionRunner