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

docs_raw/source/components/agents/value_optimization/dqn.rst

@@ -0,0 +1,37 @@
+Deep Q Networks
+===============
+
+**Actions space:** Discrete
+
+**References:** `Playing Atari with Deep Reinforcement Learning <https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf>`_
+
+Network Structure
+-----------------
+
+.. image:: /_static/img/design_imgs/dqn.png
+   :align: center
+
+Algorithm Description
+---------------------
+
+Training the network
+++++++++++++++++++++
+
+1. Sample a batch of transitions from the replay buffer.
+
+2. Using the next states from the sampled batch, run the target network to calculate the :math:`Q` values for each of
+   the actions :math:`Q(s_{t+1},a)`, and keep only the maximum value for each state.
+
+3. In order to zero out the updates for the actions that were not played (resulting from zeroing the MSE loss),
+   use the current states from the sampled batch, and run the online network to get the current Q values predictions.
+   Set those values as the targets for the actions that were not actually played.
+
+4. For each action that was played, use the following equation for calculating the targets of the network:​                                                         $$ y_t=r(s_t,a_t)+γ\cdot max_a {Q(s_{t+1},a)} $$ 
+   :math:`y_t=r(s_t,a_t )+\gamma \cdot max_a Q(s_{t+1})`
+
+5. Finally, train the online network using the current states as inputs, and with the aforementioned targets.
+
+6. Once in every few thousand steps, copy the weights from the online network to the target network.
+
+
+.. autoclass:: rl_coach.agents.dqn_agent.DQNAlgorithmParameters