diff --git a/rl_coach/graph_managers/graph_manager.py b/rl_coach/graph_managers/graph_manager.py
index 44a5df8..9e23610 100644
--- a/rl_coach/graph_managers/graph_manager.py
+++ b/rl_coach/graph_managers/graph_manager.py
@@ -541,28 +541,22 @@ class GraphManager(object):
             if self.evaluate(self.evaluation_steps):
                 break
 
-    def _restore_checkpoint_tf(self, checkpoint_path: str):
-        import tensorflow as tf
-        variables = {}
-        reader = tf.contrib.framework.load_checkpoint(checkpoint_path)
-        for var_name, _ in reader.get_variable_to_shape_map().items():
-            # Load the variable
-            var = reader.get_tensor(var_name)
-
-            # Set the new name
-            new_name = var_name
-            new_name = new_name.replace('global/', 'online/')
-            variables[new_name] = var
-
-        for v in self.variables_to_restore:
-            self.sess.run(v.assign(variables[v.name.split(':')[0]]))
-
     def restore_checkpoint(self):
         self.verify_graph_was_created()
 
         # TODO: find better way to load checkpoints that were saved with a global network into the online network
         if self.task_parameters.checkpoint_restore_dir:
-            checkpoint = get_checkpoint_state(self.task_parameters.checkpoint_restore_dir)
+            if self.task_parameters.framework_type == Frameworks.tensorflow:
+                # TODO-fixme checkpointing
+                # MonitoredTrainingSession manages save/restore checkpoints autonomously. Doing so,
+                # it creates it own names for the saved checkpoints, which do not match the "{}_Step-{}.ckpt" filename
+                # pattern. The names used are maintained in a CheckpointState protobuf file named 'checkpoint'. Using
+                # Coach's '.coach_checkpoint' protobuf file, results in an error when trying to restore the model, as
+                # the checkpoint names defined do not match the actual checkpoint names.
+                checkpoint = self._get_checkpoint_state_tf()
+            else:
+                checkpoint = get_checkpoint_state(self.task_parameters.checkpoint_restore_dir)
+
             if checkpoint is None:
                 screen.warning("No checkpoint to restore in: {}".format(self.task_parameters.checkpoint_restore_dir))
             else:
@@ -571,6 +565,10 @@ class GraphManager(object):
 
             [manager.restore_checkpoint(self.task_parameters.checkpoint_restore_dir) for manager in self.level_managers]
 
+    def _get_checkpoint_state_tf(self):
+        import tensorflow as tf
+        return tf.train.get_checkpoint_state(self.task_parameters.checkpoint_restore_dir)
+
     def occasionally_save_checkpoint(self):
         # only the chief process saves checkpoints
         if self.task_parameters.checkpoint_save_secs \