diff --git a/doc/source/rllib/core-concepts.rst b/doc/source/rllib/core-concepts.rst
index e62630a09e45..6d65b6443763 100644
--- a/doc/source/rllib/core-concepts.rst
+++ b/doc/source/rllib/core-concepts.rst
@@ -32,7 +32,7 @@ An environment in RL is the agent's world, it is a simulation of the problem to
 An RLlib environment consists of: 
 
 1. all possible actions (**action space**)
-2. a complete omniscient description of the environment, nothing hidden (**state space**)
+2. a complete description of the environment, nothing hidden (**state space**)
 3. an observation by the agent of certain parts of the state (**observation space**)
 4. **reward**, which is the only feedback the agent receives per action.
 
diff --git a/doc/source/rllib/index.rst b/doc/source/rllib/index.rst
index 7192bf8262a7..b19a80491ad8 100644
--- a/doc/source/rllib/index.rst
+++ b/doc/source/rllib/index.rst
@@ -66,7 +66,7 @@ To be able to run our Atari examples, you should also install:
 
 After these quick pip installs, you can start coding against RLlib.
 
-Here is an example of running a PPO Trainer on the "`Taxi domain <https://www.gymlibrary.ml/environments/toy_text/taxi/>`_"
+Here is an example of running a PPO Trainer on the `Taxi domain <https://www.gymlibrary.ml/environments/toy_text/taxi/>`_
 for a few training iterations, then perform a single evaluation loop
 (with rendering enabled):
 
diff --git a/doc/source/rllib/rllib-env.rst b/doc/source/rllib/rllib-env.rst
index 77fa27b645bf..3725538cd7e9 100644
--- a/doc/source/rllib/rllib-env.rst
+++ b/doc/source/rllib/rllib-env.rst
@@ -13,6 +13,8 @@ RLlib works with several different types of environments, including `OpenAI Gym
 
 .. image:: images/rllib-envs.svg
 
+.. _configuring-environments:
+
 Configuring Environments
 ------------------------