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

agents/bootstrapped_dqn_agent.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.
@@ -13,17 +13,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import numpy as np
 
-from agents.value_optimization_agent import *
-
+from agents import value_optimization_agent as voa
+import utils
 
 # Bootstrapped DQN - https://arxiv.org/pdf/1602.04621.pdf
-class BootstrappedDQNAgent(ValueOptimizationAgent):
+class BootstrappedDQNAgent(voa.ValueOptimizationAgent):
     def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
-        ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
+        voa.ValueOptimizationAgent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
 
     def reset_game(self, do_not_reset_env=False):
-        ValueOptimizationAgent.reset_game(self, do_not_reset_env)
+        voa.ValueOptimizationAgent.reset_game(self, do_not_reset_env)
         self.exploration_policy.select_head()
 
     def learn_from_batch(self, batch):
@@ -51,8 +52,8 @@ class BootstrappedDQNAgent(ValueOptimizationAgent):
 
         return total_loss
 
-    def act(self, phase=RunPhase.TRAIN):
-        ValueOptimizationAgent.act(self, phase)
+    def act(self, phase=utils.RunPhase.TRAIN):
+        voa.ValueOptimizationAgent.act(self, phase)
         mask = np.random.binomial(1, self.tp.exploration.bootstrapped_data_sharing_probability,
                                   self.tp.exploration.architecture_num_q_heads)
         self.memory.update_last_transition_info({'mask': mask})