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/imitation/bc.rst

@@ -0,0 +1,29 @@
+Behavioral Cloning
+==================
+
+**Actions space:** Discrete | Continuous
+
+Network Structure
+-----------------
+
+.. image:: /_static/img/design_imgs/pg.png
+   :align: center
+
+
+Algorithm Description
+---------------------
+
+Training the network
+++++++++++++++++++++
+
+The replay buffer contains the expert demonstrations for the task.
+These demonstrations are given as state, action tuples, and with no reward.
+The training goal is to reduce the difference between the actions predicted by the network and the actions taken by
+the expert for each state.
+
+1. Sample a batch of transitions from the replay buffer.
+2. Use the current states as input to the network, and the expert actions as the targets of the network.
+3. For the network head, we use the policy head, which uses the cross entropy loss function.
+
+
+.. autoclass:: rl_coach.agents.bc_agent.BCAlgorithmParameters

docs_raw/source/components/agents/imitation/cil.rst

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+Conditional Imitation Learning
+==============================
+
+**Actions space:** Discrete | Continuous
+
+**References:** `End-to-end Driving via Conditional Imitation Learning <https://arxiv.org/abs/1710.02410>`_
+
+Network Structure
+-----------------
+
+.. image:: /_static/img/design_imgs/cil.png
+   :align: center
+
+
+Algorithm Description
+---------------------
+
+Training the network
+++++++++++++++++++++
+
+The replay buffer contains the expert demonstrations for the task.
+These demonstrations are given as state, action tuples, and with no reward.
+The training goal is to reduce the difference between the actions predicted by the network and the actions taken by
+the expert for each state.
+In conditional imitation learning, each transition is assigned a class, which determines the goal that was pursuit
+in that transitions. For example, 3 possible classes can be: turn right, turn left and follow lane.
+
+1. Sample a batch of transitions from the replay buffer, where the batch is balanced, meaning that an equal number
+   of transitions will be sampled from each class index.
+2. Use the current states as input to the network, and assign the expert actions as the targets of the network heads
+   corresponding to the state classes. For the other heads, set the targets to match the currently predicted values,
+   so that the loss for the other heads will be zeroed out.
+3. We use a regression head, that minimizes the MSE loss between the network predicted values and the target values.
+
+
+.. autoclass:: rl_coach.agents.cil_agent.CILAlgorithmParameters