Batch RL Tutorial (#372)

docs_raw/source/features/batch_rl.rst

@@ -0,0 +1,18 @@
+Batch Reinforcement Learning
+============================
+
+Coach supports Batch Reinforcement Learning, where learning is based solely on a (fixed) batch of data.
+In Batch RL, we are given a dataset of experience, which was collected using some (one or more) deployed policies, and we would
+like to use it to learn a better policy than what was used to collect the dataset.
+There is no simulator to interact with, and so we cannot collect any new data, meaning we often cannot explore the MDP any further.
+To make things even harder, we would also like to use the dataset in order to evaluate the newly learned policy
+(using off-policy evaluation), since we do not have a simulator which we can use to evaluate the policy on.
+Batch RL is also often beneficial in cases where we just want to separate the inference (data collection) from the
+training process of a new policy. This is often the case where we have a system on which we could quite easily deploy a policy
+and collect experience data, but cannot easily use that system's setup to online train a new policy (as is often the
+case with more standard RL algorithms).
+
+Coach supports (almost) all of the integrated off-policy algorithms with Batch RL.
+
+A lot more details and example usage can be found in the
+`tutorial <https://github.com/NervanaSystems/coach/blob/master/tutorials/4.%20Batch%20Reinforcement%20Learning.ipynb>`_.

docs_raw/source/features/index.rst

@@ -7,4 +7,5 @@ Features
 
    algorithms
    environments
-   benchmarks
+   benchmarks
+   batch_rl