diff --git a/README.rst b/README.rst
index acd94fccc..37a33e507 100644
--- a/README.rst
+++ b/README.rst
@@ -133,6 +133,8 @@ If TensorBoard is installed, automatically visualize all trial results:
 RLlib Quick Start
 -----------------
 
+.. image:: https://github.com/ray-project/ray/raw/master/doc/source/rllib-stack.svg
+
 `RLlib`_ is an open-source library for reinforcement learning built on top of Ray that offers both high scalability and a unified API for a variety of applications.
 
 .. code-block:: bash
diff --git a/doc/source/rllib.rst b/doc/source/rllib.rst
index 5c30caeb1..6dedc4a67 100644
--- a/doc/source/rllib.rst
+++ b/doc/source/rllib.rst
@@ -52,10 +52,10 @@ Policies can be implemented using `any framework <https://github.com/ray-project
       return -tf.reduce_mean(
           action_dist.logp(train_batch["actions"]) * train_batch["rewards"])
 
-    # <class 'ray.rllib.policy.tf_policy_template.MyTFPolicy'>
-    MyTFPolicy = build_tf_policy(
-        name="MyTFPolicy",
-        loss_fn=policy_gradient_loss)
+  # <class 'ray.rllib.policy.tf_policy_template.MyTFPolicy'>
+  MyTFPolicy = build_tf_policy(
+      name="MyTFPolicy",
+      loss_fn=policy_gradient_loss)
 
 Sample Batches
 ~~~~~~~~~~~~~~