diff --git a/doc/source/rllib-concepts.rst b/doc/source/rllib-concepts.rst
index caf02e35913c..1eaca7d8d309 100644
--- a/doc/source/rllib-concepts.rst
+++ b/doc/source/rllib-concepts.rst
@@ -131,7 +131,7 @@ Let's modify our policy loss to include rewards summed over time. To enable this
 
 The ``postprocess_advantages()`` function above uses calls RLlib's ``compute_advantages`` function to compute advantages for each timestep. If you re-run the trainer with this improved policy, you'll find that it quickly achieves the max reward of 200.
 
-You might be wondering how RLlib makes the advantages placeholder automatically available as ``batch_tensors[Postprocessing.ADVANTAGES]``. When building your policy, RLlib will create a "dummy" trajectory batch where all observations, actions, rewards, etc. are zeros. It then calls your ``postprocess_fn``, and generates TF placeholders based on the numpy shapes of the postprocessed batch. RLlib tracks which placeholders that ``loss_fn`` and ``stats_fn`` access, and then feeds the corresponding sample data into those placeholders during loss optimization.
+You might be wondering how RLlib makes the advantages placeholder automatically available as ``batch_tensors[Postprocessing.ADVANTAGES]``. When building your policy, RLlib will create a "dummy" trajectory batch where all observations, actions, rewards, etc. are zeros. It then calls your ``postprocess_fn``, and generates TF placeholders based on the numpy shapes of the postprocessed batch. RLlib tracks which placeholders that ``loss_fn`` and ``stats_fn`` access, and then feeds the corresponding sample data into those placeholders during loss optimization. You can also access these placeholders via ``policy.get_placeholder(<name>)``.
 
 **Example 1: Proximal Policy Optimization**