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

environments/gym_environment_wrapper.py

@@ -13,40 +13,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import random
 
-import sys
-from logger import *
 import gym
 import numpy as np
-import time
-import random
-try:
-    import roboschool
-    from OpenGL import GL
-except ImportError:
-    from logger import failed_imports
-    failed_imports.append("RoboSchool")
 
-try:
-    from gym_extensions.continuous import mujoco
-except:
-    from logger import failed_imports
-    failed_imports.append("GymExtensions")
-
-try:
-    import pybullet_envs
-except ImportError:
-    from logger import failed_imports
-    failed_imports.append("PyBullet")
-
-from gym import wrappers
-from utils import force_list, RunPhase
-from environments.environment_wrapper import EnvironmentWrapper
+from environments import environment_wrapper as ew
+import utils
 
 
-class GymEnvironmentWrapper(EnvironmentWrapper):
+class GymEnvironmentWrapper(ew.EnvironmentWrapper):
     def __init__(self, tuning_parameters):
-        EnvironmentWrapper.__init__(self, tuning_parameters)
+        ew.EnvironmentWrapper.__init__(self, tuning_parameters)
 
         # env parameters
         if ':' in self.env_id:
@@ -124,7 +102,7 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
 
     def _update_state(self):
         if hasattr(self.env, 'env') and hasattr(self.env.env, 'ale'):
-            if self.phase == RunPhase.TRAIN and hasattr(self, 'current_ale_lives'):
+            if self.phase == utils.RunPhase.TRAIN and hasattr(self, 'current_ale_lives'):
                 # signal termination for life loss
                 if self.current_ale_lives != self.env.env.ale.lives():
                     self.done = True
@@ -146,7 +124,7 @@ class GymEnvironmentWrapper(EnvironmentWrapper):
             if type(action_idx) == int and action_idx == 0:
                 # deal with the "reset" action 0
                 action = [0] * self.env.action_space.shape[0]
-            action = np.array(force_list(action))
+            action = np.array(utils.force_list(action))
             # removing redundant dimensions such that the action size will match the expected action size from gym
             if action.shape != self.env.action_space.shape:
                 action = np.squeeze(action)