Adding checkpointing framework (#74)

* Adding checkpointing framework as well as mxnet checkpointing implementation.

- MXNet checkpoint for each network is saved in a separate file.

* Adding checkpoint restore for mxnet to graph-manager

* Add unit-test for get_checkpoint_state()

* Added match.group() to fix unit-test failing on CI

* Added ONNX export support for MXNet

rl_coach/architectures/network_wrapper.py

@@ -18,6 +18,7 @@ from typing import List, Tuple
 
 from rl_coach.base_parameters import Frameworks, AgentParameters
 from rl_coach.logger import failed_imports
+from rl_coach.saver import SaverCollection
 from rl_coach.spaces import SpacesDefinition
 try:
     import tensorflow as tf
@@ -251,3 +252,25 @@ class NetworkWrapper(object):
         result.append(str(self.online_network))
         result.append("")
         return '\n'.join(result)
+
+    def collect_savers(self, parent_path_suffix: str) -> SaverCollection:
+        """
+        Collect all of network's savers for global or online network
+        Note: global, online, and target network are all copies fo the same network which parameters that are
+            updated at different rates. So we only need to save one of the networks; the one that holds the most
+            recent parameters. target network is created for some agents and used for stabilizing training by
+            updating parameters from online network at a slower rate. As a result, target network never contains
+            the most recent set of parameters. In single-worker training, no global network is created and online
+            network contains the most recent parameters. In vertical distributed training with more than one worker,
+            global network is updated by all workers and contains the most recent parameters.
+            Therefore preference is given to global network if it exists, otherwise online network is used
+            for saving.
+        :param parent_path_suffix: path suffix of the parent of the network wrapper
+            (e.g. could be name of level manager plus name of agent)
+        :return: collection of all checkpoint objects
+        """
+        if self.global_network:
+            savers = self.global_network.collect_savers(parent_path_suffix)
+        else:
+            savers = self.online_network.collect_savers(parent_path_suffix)
+        return savers