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/double_dqn.rst

@@ -0,0 +1,35 @@
+Double DQN
+==========
+
+**Actions space:** Discrete
+
+**References:** `Deep Reinforcement Learning with Double Q-learning <https://arxiv.org/abs/1509.06461.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 online network in order to find the $Q$ maximizing
+   action :math:`argmax_a Q(s_{t+1},a)`. For these actions, use the corresponding next states and run the target
+   network to calculate :math:`Q(s_{t+1},argmax_a Q(s_{t+1},a))`.
+
+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:
+   :math:`y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},argmax_a Q(s_{t+1},a))`
+
+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.