diff --git a/docs/introduction/train_agent.md b/docs/introduction/train_agent.md
index 7117e4d9a..564be2f80 100644
--- a/docs/introduction/train_agent.md
+++ b/docs/introduction/train_agent.md
@@ -115,8 +115,11 @@ start_epsilon = 1.0
 epsilon_decay = start_epsilon / (n_episodes / 2)  # reduce the exploration over time
 final_epsilon = 0.1
 
+env = gym.make("Blackjack-v1", sab=False)
+env = gym.wrappers.RecordEpisodeStatistics(env, deque_size=n_episodes)
+
 agent = BlackjackAgent(
-    env,
+    env=env,
     learning_rate=learning_rate,
     initial_epsilon=start_epsilon,
     epsilon_decay=epsilon_decay,
@@ -129,9 +132,6 @@ Info: The current hyperparameters are set to quickly train a decent agent. If yo
 ```python
 from tqdm import tqdm
 
-env = gym.make("Blackjack-v1", sab=False)
-env = gym.wrappers.RecordEpisodeStatistics(env, deque_size=n_episodes)
-
 for episode in tqdm(range(n_episodes)):
     obs, info = env.reset()
     done = False
@@ -151,6 +151,34 @@ for episode in tqdm(range(n_episodes)):
     agent.decay_epsilon()
 ```
 
+You can use `matplotlib` to visualize the training reward and length.
+
+```python
+from matplotlib import pyplot as plt
+# visualize the episode rewards, episode length and training error in one figure
+fig, axs = plt.subplots(1, 3, figsize=(20, 8))
+
+# np.convolve will compute the rolling mean for 100 episodes
+
+axs[0].plot(np.convolve(env.return_queue, np.ones(100)))
+axs[0].set_title("Episode Rewards")
+axs[0].set_xlabel("Episode")
+axs[0].set_ylabel("Reward")
+
+axs[1].plot(np.convolve(env.length_queue, np.ones(100)))
+axs[1].set_title("Episode Lengths")
+axs[1].set_xlabel("Episode")
+axs[1].set_ylabel("Length")
+
+axs[2].plot(np.convolve(agent.training_error, np.ones(100)))
+axs[2].set_title("Training Error")
+axs[2].set_xlabel("Episode")
+axs[2].set_ylabel("Temporal Difference")
+
+plt.tight_layout()
+plt.show()
+```
+
 ![](../_static/img/tutorials/blackjack_training_plots.png "Training Plot")
 
 ## Visualising the policy