diff --git a/doc/source/rllib-env.rst b/doc/source/rllib-env.rst
index d5f5f6eba..b2d959e7d 100644
--- a/doc/source/rllib-env.rst
+++ b/doc/source/rllib-env.rst
@@ -130,10 +130,6 @@ When using remote envs, you can control the batching level for inference with ``
 Multi-Agent and Hierarchical
 ----------------------------
 
-.. note::
-
-   Learn more about multi-agent reinforcement learning in RLlib by checking out some of the `code examples <rllib-examples.html#multi-agent-and-hierarchical>`__ or reading the `blog post <https://bair.berkeley.edu/blog/2018/12/12/rllib/>`__.
-
 A multi-agent environment is one which has multiple acting entities per step, e.g., in a traffic simulation, there may be multiple "car" and "traffic light" agents in the environment. The model for multi-agent in RLlib as follows: (1) as a user you define the number of policies available up front, and (2) a function that maps agent ids to policy ids. This is summarized by the below figure:
 
 .. image:: multi-agent.svg
diff --git a/doc/source/rllib-examples.rst b/doc/source/rllib-examples.rst
index 036b6f8ee..995e9d893 100644
--- a/doc/source/rllib-examples.rst
+++ b/doc/source/rllib-examples.rst
@@ -13,6 +13,14 @@ Tuned Examples
 - `Atari benchmarks <https://github.com/ray-project/rl-experiments>`__:
    Collection of reasonably optimized Atari results.
 
+Blog Posts
+----------
+
+- `Scaling Multi-Agent Reinforcement Learning <http://bair.berkeley.edu/blog/2018/12/12/rllib>`__:
+   This blog post is a brief tutorial on multi-agent RL and its design in RLlib.
+- `Functional RL with Keras and TensorFlow Eager <https://medium.com/riselab/functional-rl-with-keras-and-tensorflow-eager-7973f81d6345>`__:
+   Exploration of a functional paradigm for implementing reinforcement learning (RL) algorithms.
+
 Training Workflows
 ------------------
 
diff --git a/doc/source/rllib.rst b/doc/source/rllib.rst
index bdc47a108..7a5d47a4a 100644
--- a/doc/source/rllib.rst
+++ b/doc/source/rllib.rst
@@ -10,7 +10,7 @@ To get started, take a look over the `custom env example <https://github.com/ray
 RLlib in 60 seconds
 -------------------
 
-The following is a whirlwind overview of RLlib. See also the full `table of contents <rllib-toc.html>`__ for a more in-depth guide including the `list of built-in algorithms <rllib-toc.html#algorithms>`__.
+The following is a whirlwind overview of RLlib. For a more in-depth guide, see also the `full table of contents <rllib-toc.html>`__ and `RLlib blog posts <rllib-examples.html#blog-posts>`__. You may also want to skim the `list of built-in algorithms <rllib-toc.html#algorithms>`__.
 
 Running RLlib
 ~~~~~~~~~~~~~