moving the docs to github

docs_raw/docs/contributing/add_agent.md

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+<!-- language-all: python -->
+
+Coach's modularity makes adding an agent a simple and clean task, that involves the following steps:
+
+1.	Implement your algorithm in a new file under the agents directory. The agent can inherit base classes such as **ValueOptimizationAgent** or **ActorCriticAgent**, or the more generic **Agent** base class.
+    
+    * **ValueOptimizationAgent**, **PolicyOptimizationAgent** and **Agent** are abstract classes. 
+    learn_from_batch() should be overriden with the desired behavior for the algorithm being implemented. If deciding to inherit from **Agent**, also choose_action() should be overriden.       
+        
+    
+            def learn_from_batch(self, batch):
+                """
+                Given a batch of transitions, calculates their target values and updates the network.
+                :param batch: A list of transitions
+                :return: The loss of the training
+                """
+                pass
+                
+            def choose_action(self, curr_state, phase=RunPhase.TRAIN):
+                """
+                choose an action to act with in the current episode being played. Different behavior might be exhibited when training
+                 or testing.
+                 
+                :param curr_state: the current state to act upon.  
+                :param phase: the current phase: training or testing.
+                :return: chosen action, some action value describing the action (q-value, probability, etc)
+                """
+                pass
+                
+            
+       
+    * Make sure to add your new agent to **agents/\_\_init\_\_.py**
+    
+2.	Implement your agent's specific network head, if needed, at the implementation for the framework of your choice. For example **architectures/neon_components/heads.py**. The head will inherit the generic base class Head.
+    A new output type should be added to configurations.py, and a mapping between the new head and output type should be defined in the get_output_head() function at **architectures/neon_components/general_network.py**
+3.	Define a new configuration class at configurations.py, which includes the new agent name in the **type** field, the new output type in the **output_types** field, and assigning default values to hyperparameters.
+4.	(Optional) Define a preset using the new agent type with a given environment, and the hyperparameters that should be used for training on that environment.
+