diff --git a/doc/source/rllib-concepts.rst b/doc/source/rllib-concepts.rst
index 00fc9daf2..1ced167ee 100644
--- a/doc/source/rllib-concepts.rst
+++ b/doc/source/rllib-concepts.rst
@@ -6,7 +6,7 @@ This page describes the internal concepts used to implement algorithms in RLlib.
 Policies
 --------
 
-Policy classes encapsulate the core numerical components of RL algorithms. This typically includes the policy model that determines actions to take, a trajectory postprocessor for experiences, and a loss function to improve the policy given postprocessed experiences. For a simple example, see the policy gradients `policy definition <https://github.com/ray-project/ray/blob/master/rllib/agents/pg/pg_policy.py>`__.
+Policy classes encapsulate the core numerical components of RL algorithms. This typically includes the policy model that determines actions to take, a trajectory postprocessor for experiences, and a loss function to improve the policy given postprocessed experiences. For a simple example, see the policy gradients `policy definition <https://github.com/ray-project/ray/blob/master/rllib/agents/pg/pg_tf_policy.py>`__.
 
 Most interaction with deep learning frameworks is isolated to the `Policy interface <https://github.com/ray-project/ray/blob/master/rllib/policy/policy.py>`__, allowing RLlib to support multiple frameworks. To simplify the definition of policies, RLlib includes `Tensorflow <#building-policies-in-tensorflow>`__ and `PyTorch-specific <#building-policies-in-pytorch>`__ templates. You can also write your own from scratch. Here is an example: