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/dfp_agent.py

@@ -13,17 +13,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+import numpy as np
 
-from agents.agent import *
+from agents import agent
+from architectures import network_wrapper as nw
+import utils
 
 
 # Direct Future Prediction Agent - http://vladlen.info/papers/learning-to-act.pdf
-class DFPAgent(Agent):
+class DFPAgent(agent.Agent):
     def __init__(self, env, tuning_parameters, replicated_device=None, thread_id=0):
-        Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
+        agent.Agent.__init__(self, env, tuning_parameters, replicated_device, thread_id)
         self.current_goal = self.tp.agent.goal_vector
-        self.main_network = NetworkWrapper(tuning_parameters, False, self.has_global, 'main',
-                                           self.replicated_device, self.worker_device)
+        self.main_network = nw.NetworkWrapper(tuning_parameters, False, self.has_global, 'main',
+                                              self.replicated_device, self.worker_device)
         self.networks.append(self.main_network)
 
     def learn_from_batch(self, batch):
@@ -45,7 +48,7 @@ class DFPAgent(Agent):
 
         return total_loss
 
-    def choose_action(self, curr_state, phase=RunPhase.TRAIN):
+    def choose_action(self, curr_state, phase=utils.RunPhase.TRAIN):
         # convert to batch so we can run it through the network
         observation = np.expand_dims(np.array(curr_state['observation']), 0)
         measurements = np.expand_dims(np.array(curr_state['measurements']), 0)
@@ -66,7 +69,7 @@ class DFPAgent(Agent):
                                                self.tp.agent.future_measurements_weights)
 
         # choose action according to the exploration policy and the current phase (evaluating or training the agent)
-        if phase == RunPhase.TRAIN:
+        if phase == utils.RunPhase.TRAIN:
             action = self.exploration_policy.get_action(action_values)
         else:
             action = np.argmax(action_values)