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/agents/policy_gradients_agent.py

@@ -42,6 +42,27 @@ class PolicyGradientNetworkParameters(NetworkParameters):
 
 
 class PolicyGradientAlgorithmParameters(AlgorithmParameters):
+    """
+    :param policy_gradient_rescaler: (PolicyGradientRescaler)
+        The rescaler type to use for the policy gradient loss. For policy gradients, we calculate log probability of
+        the action and then multiply it by the policy gradient rescaler. The most basic rescaler is the discounter
+        return, but there are other rescalers that are intended for reducing the variance of the updates.
+
+    :param apply_gradients_every_x_episodes: (int)
+        The number of episodes between applying the accumulated gradients to the network. After every
+        num_steps_between_gradient_updates steps, the agent will calculate the gradients for the collected data,
+        it will then accumulate it in internal accumulators, and will only apply them to the network once in every
+        apply_gradients_every_x_episodes episodes.
+
+    :param beta_entropy: (float)
+        A factor which defines the amount of entropy regularization to apply to the network. The entropy of the actions
+        will be added to the loss and scaled by the given beta factor.
+
+    :param num_steps_between_gradient_updates: (int)
+        The number of steps between calculating gradients for the collected data. In the A3C paper, this parameter is
+        called t_max. Since this algorithm is on-policy, only the steps collected between each two gradient calculations
+        are used in the batch.
+    """
     def __init__(self):
         super().__init__()
         self.policy_gradient_rescaler = PolicyGradientRescaler.FUTURE_RETURN_NORMALIZED_BY_TIMESTEP