diff --git a/docs/introduction/train_agent.md b/docs/introduction/train_agent.md
index 4c0f2542e..461a0d9b1 100644
--- a/docs/introduction/train_agent.md
+++ b/docs/introduction/train_agent.md
@@ -155,37 +155,48 @@ 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)/100))
-axs[0].set_title("Episode Rewards")
-axs[0].set_xlabel("Episode")
-axs[0].set_ylabel("Reward")
+def get_moving_avgs(arr, window, convolution_mode):
+    return np.convolve(
+        np.array(arr).flatten(),
+        np.ones(window),
+        mode=convolution_mode
+    ) / window
+
+# Smooth over a 500 episode window
+rolling_length = 500
+fig, axs = plt.subplots(ncols=3, figsize=(12, 5))
+
+axs[0].set_title("Episode rewards")
+reward_moving_average = get_moving_avgs(
+    env.return_queue,
+    rolling_length,
+    "valid"
+)
+axs[0].plot(range(len(reward_moving_average)), reward_moving_average)
 
-axs[1].plot(np.convolve(env.length_queue, np.ones(100)/100))
-axs[1].set_title("Episode Lengths")
-axs[1].set_xlabel("Episode")
-axs[1].set_ylabel("Length")
+axs[1].set_title("Episode lengths")
+length_moving_average = get_moving_avgs(
+    env.length_queue,
+    rolling_length,
+    "valid"
+)
+axs[1].plot(range(len(length_moving_average)), length_moving_average)
 
-axs[2].plot(np.convolve(agent.training_error, np.ones(100)/100))
 axs[2].set_title("Training Error")
-axs[2].set_xlabel("Episode")
-axs[2].set_ylabel("Temporal Difference")
-
+training_error_moving_average = get_moving_avgs(
+    agent.training_error,
+    rolling_length,
+    "same"
+)
+axs[2].plot(range(len(training_error_moving_average)), training_error_moving_average)
 plt.tight_layout()
 plt.show()
-```
-
-![](../_static/img/tutorials/blackjack_training_plots.png "Training Plot")
 
-## Visualising the policy
 
-![](../_static/img/tutorials/blackjack_with_usable_ace.png "With a usable ace")
+```
 
-![](../_static/img/tutorials/blackjack_without_usable_ace.png "Without a usable ace")
+![](../_static/img/tutorials/blackjack_training_plots.png "Training Plot")
 
 Hopefully this tutorial helped you get a grip of how to interact with Gymnasium environments and sets you on a journey to solve many more RL challenges.