Cleanup imports.

Till now, most of the modules were importing all of the module objects
(variables, classes, functions, other imports) into module namespace,
which potentially could (and was) cause of unintentional use of class or
methods, which was indirect imported.

With this patch, all the star imports were substituted with top-level
module, which provides desired class or function.

Besides, all imports where sorted (where possible) in a way pep8[1]
suggests - first are imports from standard library, than goes third
party imports (like numpy, tensorflow etc) and finally coach modules.
All of those sections are separated by one empty line.

[1] https://www.python.org/dev/peps/pep-0008/#imports

memories/episodic_experience_replay.py

@@ -1,5 +1,5 @@
 #
-# Copyright (c) 2017 Intel Corporation 
+# Copyright (c) 2017 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -13,24 +13,25 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import typing
 
-from memories.memory import *
-import threading
-from typing import Union
+import numpy as np
+
+from memories import memory
 
 
-class EpisodicExperienceReplay(Memory):
+class EpisodicExperienceReplay(memory.Memory):
     def __init__(self, tuning_parameters):
         """
         :param tuning_parameters: A Preset class instance with all the running paramaters
         :type tuning_parameters: Preset
         """
-        Memory.__init__(self, tuning_parameters)
+        memory.Memory.__init__(self, tuning_parameters)
         self.tp = tuning_parameters
         self.max_size_in_episodes = tuning_parameters.agent.num_episodes_in_experience_replay
         self.max_size_in_transitions = tuning_parameters.agent.num_transitions_in_experience_replay
         self.discount = tuning_parameters.agent.discount
-        self.buffer = [Episode()]  # list of episodes
+        self.buffer = [memory.Episode()]  # list of episodes
         self.transitions = []
         self._length = 1
         self._num_transitions = 0
@@ -96,7 +97,7 @@ class EpisodicExperienceReplay(Memory):
 
     def store(self, transition):
         if len(self.buffer) == 0:
-            self.buffer.append(Episode())
+            self.buffer.append(memory.Episode())
         last_episode = self.buffer[-1]
         last_episode.insert(transition)
         self.transitions.append(transition)
@@ -109,7 +110,7 @@ class EpisodicExperienceReplay(Memory):
                                            n_step_return=self.tp.agent.n_step)
             self.buffer[-1].update_measurements_targets(self.tp.agent.num_predicted_steps_ahead)
             # self.buffer[-1].update_actions_probabilities() # used for off-policy policy optimization
-            self.buffer.append(Episode())
+            self.buffer.append(memory.Episode())
 
         self.enforce_length()
 
@@ -148,7 +149,7 @@ class EpisodicExperienceReplay(Memory):
     def get(self, index):
         return self.get_episode(index)
 
-    def get_last_complete_episode(self) -> Union[None, Episode]:
+    def get_last_complete_episode(self) -> typing.Union[None, memory.Episode]:
         """
         Returns the last complete episode in the memory or None if there are no complete episodes
         :return: None or the last complete episode
@@ -170,7 +171,7 @@ class EpisodicExperienceReplay(Memory):
 
     def clean(self):
         self.transitions = []
-        self.buffer = [Episode()]
+        self.buffer = [memory.Episode()]
         self._length = 1
         self._num_transitions = 0
         self._num_transitions_in_complete_episodes = 0