update of api docstrings across coach and tutorials [WIP] (#91)

* updating the documentation website
* adding the built docs
* update of api docstrings across coach and tutorials 0-2
* added some missing api documentation
* New Sphinx based documentation

rl_coach/architectures/network_wrapper.py

@@ -34,7 +34,11 @@ except ImportError:
 
 class NetworkWrapper(object):
     """
-    Contains multiple networks and managers syncing and gradient updates
+    The network wrapper contains multiple copies of the same network, each one with a different set of weights which is
+    updating in a different time scale. The network wrapper will always contain an online network.
+    It will contain an additional slow updating target network if it was requested by the user,
+    and it will contain a global network shared between different workers, if Coach is run in a single-node
+    multi-process distributed mode. The network wrapper contains functionality for managing these networks and syncing
     between them.
     """
     def __init__(self, agent_parameters: AgentParameters, has_target: bool, has_global: bool, name: str,
@@ -98,6 +102,7 @@ class NetworkWrapper(object):
     def sync(self):
         """
         Initializes the weights of the networks to match each other
+
         :return:
         """
         self.update_online_network()
@@ -106,6 +111,7 @@ class NetworkWrapper(object):
     def update_target_network(self, rate=1.0):
         """
         Copy weights: online network >>> target network
+
         :param rate: the rate of copying the weights - 1 for copying exactly
         """
         if self.target_network:
@@ -114,6 +120,7 @@ class NetworkWrapper(object):
     def update_online_network(self, rate=1.0):
         """
         Copy weights: global network >>> online network
+
         :param rate: the rate of copying the weights - 1 for copying exactly
         """
         if self.global_network:
@@ -122,6 +129,7 @@ class NetworkWrapper(object):
     def apply_gradients_to_global_network(self, gradients=None):
         """
         Apply gradients from the online network on the global network
+
         :param gradients: optional gradients that will be used instead of teh accumulated gradients
         :return:
         """
@@ -135,6 +143,7 @@ class NetworkWrapper(object):
     def apply_gradients_to_online_network(self, gradients=None):
         """
         Apply gradients from the online network on itself
+
         :return:
         """
         if gradients is None:
@@ -144,6 +153,7 @@ class NetworkWrapper(object):
     def train_and_sync_networks(self, inputs, targets, additional_fetches=[], importance_weights=None):
         """
         A generic training function that enables multi-threading training using a global network if necessary.
+
         :param inputs: The inputs for the network.
         :param targets: The targets corresponding to the given inputs
         :param additional_fetches: Any additional tensor the user wants to fetch
@@ -160,6 +170,7 @@ class NetworkWrapper(object):
         """
         Applies the gradients accumulated in the online network to the global network or to itself and syncs the
         networks if necessary
+
         :param reset_gradients: If set to True, the accumulated gradients wont be reset to 0 after applying them to
                                 the network. this is useful when the accumulated gradients are overwritten instead
                                 if accumulated by the accumulate_gradients function. this allows reducing time
@@ -179,6 +190,7 @@ class NetworkWrapper(object):
     def parallel_prediction(self, network_input_tuples: List[Tuple]):
         """
         Run several network prediction in parallel. Currently this only supports running each of the network once.
+
         :param network_input_tuples: a list of tuples where the first element is the network (online_network,
                                      target_network or global_network) and the second element is the inputs
         :return: the outputs of all the networks in the same order as the inputs were given
@@ -188,6 +200,7 @@ class NetworkWrapper(object):
     def get_local_variables(self):
         """
         Get all the variables that are local to the thread
+
         :return: a list of all the variables that are local to the thread
         """
         local_variables = [v for v in tf.local_variables() if self.online_network.name in v.name]
@@ -198,6 +211,7 @@ class NetworkWrapper(object):
     def get_global_variables(self):
         """
         Get all the variables that are shared between threads
+
         :return: a list of all the variables that are shared between threads
         """
         global_variables = [v for v in tf.global_variables() if self.global_network.name in v.name]
@@ -206,6 +220,7 @@ class NetworkWrapper(object):
     def set_is_training(self, state: bool):
         """
         Set the phase of the network between training and testing
+
         :param state: The current state (True = Training, False = Testing)
         :return: None
         """