diff --git a/doc/source/rllib.rst b/doc/source/rllib.rst
index 3a5b1109c..ff5d9e64c 100644
--- a/doc/source/rllib.rst
+++ b/doc/source/rllib.rst
@@ -76,7 +76,7 @@ The ``train.py`` script has a number of options you can show by running
 The most important options are for choosing the environment
 with ``--env`` (any OpenAI gym environment including ones registered by the user
 can be used) and for choosing the algorithm with ``--run``
-(available options are ``PPO``, ``PG``, ``A3C``, ``ES``, ``DDPG``, ``DDPG2``, ``DQN``, ``APEX``, and ``APEX_DDPG2``).
+(available options are ``PPO``, ``PG``, ``A3C``, ``ES``, ``DDPG``, ``DDPG2``, ``DQN``, ``APEX``, and ``APEX_DDPG``).
 
 Specifying Parameters
 ~~~~~~~~~~~~~~~~~~~~~
@@ -91,7 +91,7 @@ Each algorithm has specific hyperparameters that can be set with ``--config`` -
 `DDPG <https://github.com/ray-project/ray/blob/master/python/ray/rllib/ddpg/ddpg.py>`__,
 `DDPG2 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/ddpg2/ddpg.py>`__,
 `APEX <https://github.com/ray-project/ray/blob/master/python/ray/rllib/dqn/apex.py>`__, and
-`APEX_DDPG2 <https://github.com/ray-project/ray/blob/master/python/ray/rllib/ddpg2/apex.py>`__.
+`APEX_DDPG <https://github.com/ray-project/ray/blob/master/python/ray/rllib/ddpg/apex.py>`__.
 
 In an example below, we train A3C by specifying 8 workers through the config flag.
 function that creates the env to refer to it by name. The contents of the env_config agent config field will be passed to that function to allow the environment to be configured. The return type should be an OpenAI gym.Env. For example: