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/policy_optimization/pg.rst

@@ -0,0 +1,39 @@
+Policy Gradient
+===============
+
+**Actions space:** Discrete | Continuous
+
+**References:** `Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning <http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf>`_
+
+Network Structure
+-----------------
+
+.. image:: /_static/img/design_imgs/pg.png
+   :align: center
+
+Algorithm Description
+---------------------
+Choosing an action - Discrete actions
++++++++++++++++++++++++++++++++++++++
+Run the current states through the network and get a policy distribution over the actions.
+While training, sample from the policy distribution. When testing, take the action with the highest probability.
+
+Training the network
+++++++++++++++++++++
+The policy head loss is defined as :math:`L=-log (\pi) \cdot  PolicyGradientRescaler`.
+The :code:`PolicyGradientRescaler` is used in order to reduce the policy gradient variance, which might be very noisy.
+This is done in order to reduce the variance of the updates, since noisy gradient updates might destabilize the policy's
+convergence. The rescaler is a configurable parameter and there are few options to choose from:
+
+* **Total Episode Return** - The sum of all the discounted rewards during the episode.
+* **Future Return** - Return from each transition until the end of the episode.
+* **Future Return Normalized by Episode** - Future returns across the episode normalized by the episode's mean and standard deviation.
+* **Future Return Normalized by Timestep** - Future returns normalized using running means and standard deviations,
+  which are calculated seperately for each timestep, across different episodes.
+
+Gradients are accumulated over a number of full played episodes. The gradients accumulation over several episodes
+serves the same purpose - reducing the update variance. After accumulating gradients for several episodes,
+the gradients are then applied to the network.
+
+
+.. autoclass:: rl_coach.agents.policy_gradients_agent.PolicyGradientAlgorithmParameters