cleanup

docs/introduction/train_agent.md

@@ -166,30 +166,34 @@ def get_moving_avgs(arr, window, convolution_mode):
 # Smooth over a 500 episode window
 rolling_length = 500
 fig, axs = plt.subplots(ncols=3, figsize=(12, 5))
+
 axs[0].set_title("Episode rewards")
-# compute and assign a rolling average of the data to provide a smoother graph
-reward_moving_average = (
-    get_moving_avgs(env.return_queue, rolling_length, "valid")
-    / rolling_length
+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].set_title("Episode lengths")
-length_moving_average = (
-    get_moving_avgs(env.length_queue, rolling_length, "valid")
-    / rolling_length
+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].set_title("Training Error")
-training_error_moving_average = (
-    get_moving_avgs(agent.training_error, rolling_length, "same")
-    / rolling_length
+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")