diff --git a/python/ray/rllib/examples/saving_experiences.py b/python/ray/rllib/examples/saving_experiences.py
index 7a29b0fe7b0d..d2de88302d23 100644
--- a/python/ray/rllib/examples/saving_experiences.py
+++ b/python/ray/rllib/examples/saving_experiences.py
@@ -7,6 +7,7 @@
 import gym
 import numpy as np
 
+from ray.rllib.models.preprocessors import get_preprocessor
 from ray.rllib.evaluation.sample_batch_builder import SampleBatchBuilder
 from ray.rllib.offline.json_writer import JsonWriter
 
@@ -18,6 +19,12 @@
     # simulator is available, but let's do it anyways for example purposes:
     env = gym.make("CartPole-v0")
 
+    # RLlib uses preprocessors to implement transforms such as one-hot encoding
+    # and flattening of tuple and dict observations. For CartPole a no-op
+    # preprocessor is used, but this may be relevant for more complex envs.
+    prep = get_preprocessor(env.observation_space)(env.observation_space)
+    print("The preprocessor is", prep)
+
     for eps_id in range(100):
         obs = env.reset()
         prev_action = np.zeros_like(env.action_space.sample())
@@ -31,7 +38,7 @@
                 t=t,
                 eps_id=eps_id,
                 agent_index=0,
-                obs=obs,
+                obs=prep.transform(obs),
                 actions=action,
                 action_prob=1.0,  # put the true action probability here
                 rewards=rew,
@@ -39,7 +46,7 @@
                 prev_rewards=prev_reward,
                 dones=done,
                 infos=info,
-                new_obs=new_obs)
+                new_obs=prep.transform(new_obs))
             obs = new_obs
             prev_action = action
             prev_reward = rew