diff --git a/doc/source/rllib-training.rst b/doc/source/rllib-training.rst
index b2bfc2699..25cd0d893 100644
--- a/doc/source/rllib-training.rst
+++ b/doc/source/rllib-training.rst
@@ -162,11 +162,28 @@ Tune will schedule the trials to run in parallel on your Ray cluster:
      - PPO_CartPole-v0_0_sgd_stepsize=0.01:	RUNNING [pid=21940], 16 s, 4013 ts, 22 rew
      - PPO_CartPole-v0_1_sgd_stepsize=0.001:	RUNNING [pid=21942], 27 s, 8111 ts, 54.7 rew
 
-Accessing Global State
+Accessing Policy State
 ~~~~~~~~~~~~~~~~~~~~~~
 It is common to need to access an agent's internal state, e.g., to set or get internal weights. In RLlib an agent's state is replicated across multiple *policy evaluators* (Ray actors) in the cluster. However, you can easily get and update this state between calls to ``train()`` via ``agent.optimizer.foreach_evaluator()`` or ``agent.optimizer.foreach_evaluator_with_index()``. These functions take a lambda function that is applied with the evaluator as an arg. You can also return values from these functions and those will be returned as a list.
 
-You can also access just the "master" copy of the agent state through ``agent.optimizer.local_evaluator``, but note that updates here may not be reflected in remote replicas if you have configured ``num_workers > 0``.
+You can also access just the "master" copy of the agent state through ``agent.local_evaluator``, but note that updates here may not be immediately reflected in remote replicas if you have configured ``num_workers > 0``. For example, to access the weights of a local TF policy, you can run ``agent.local_evaluator.policy_map["default"].get_weights()``. This is also equivalent to ``agent.local_evaluator.for_policy(lambda p: p.get_weights())``:
+
+.. code-block:: python
+
+    # Get weights of the local policy
+    agent.local_evaluator.policy_map["default"].get_weights()
+
+    # Same as above
+    agent.local_evaluator.for_policy(lambda p: p.get_weights())
+
+    # Get list of weights of each evaluator, including remote replicas
+    agent.optimizer.foreach_evaluator(
+        lambda ev: ev.for_policy(lambda p: p.get_weights()))
+
+    # Same as above
+    agent.optimizer.foreach_evaluator_with_index(
+        lambda ev, i: ev.for_policy(lambda p: p.get_weights()))
+
 
 REST API
 --------