diff --git a/examples/ppo_hover_eval.py b/examples/ppo_hover_eval.py
index 9874939..19a70dc 100644
--- a/examples/ppo_hover_eval.py
+++ b/examples/ppo_hover_eval.py
@@ -42,7 +42,7 @@ models_available = os.listdir(models_dir)
 for i, name in enumerate(models_available):
     print(f"{i}: {name}")
 model_idx = int(input("Enter the model index: "))  
-epoch_dir = os.path.join(models_dir, models_available[model_idx])
+num_timesteps_dir = os.path.join(models_dir, models_available[model_idx])
 
 # Next import Stable Baselines.
 try:
@@ -95,33 +95,33 @@ envs.append(gym.make("Quadrotor-v0",
 # Print out policies for the user to select.
 def extract_number(filename):
     return int(filename.split('_')[1].split('.')[0])
-epochs_list = [fname for fname in os.listdir(epoch_dir) if fname.startswith('hover_')]
-epochs_list_sorted = sorted(epochs_list, key=extract_number)
+num_timesteps_list = [fname for fname in os.listdir(num_timesteps_dir) if fname.startswith('hover_')]
+num_timesteps_list_sorted = sorted(num_timesteps_list, key=extract_number)
 
 print("Select one of the epochs:")
-for i, name in enumerate(epochs_list_sorted):
+for i, name in enumerate(num_timesteps_list_sorted):
     print(f"{i}: {name}")
-epoch_idxs = [int(input("Enter the epoch index: "))]  
+num_timesteps_idxs = [int(input("Enter the epoch index: "))]  
 
 # You can optionally just hard code a series of epochs you'd like to evaluate all at once.
-# e.g. epoch_idxs = [0, 1, 2, ...]
+# e.g. num_timesteps_idxs = [0, 1, 2, ...]
 
 # Evaluation...
-for (k, epoch_idx) in enumerate(epoch_idxs):  # For each epoch index...
+for (k, num_timesteps_idx) in enumerate(num_timesteps_idxs):  # For each num_timesteps index...
 
     print(f"[ppo_hover_eval.py]: Starting epoch {k+1} out of {len(epoch_idxs)}.")
 
     # Load the model for the appropriate epoch.
-    model_path = os.path.join(epoch_dir, epochs_list_sorted[epoch_idx])
+    model_path = os.path.join(num_timesteps_dir, num_timesteps_list_sorted[num_timesteps_idx])
     print(f"Loading model from the path {model_path}")
     model = PPO.load(model_path, env=envs[0], tensorboard_log=log_dir)
 
     # Set figure title for 3D plot.
-    fig.suptitle(f"Model: PPO/{models_available[model_idx]}, Epoch: {extract_number(epochs_list_sorted[epoch_idx]):,}")
+    fig.suptitle(f"Model: PPO/{models_available[model_idx]}, Epoch: {extract_number(num_timesteps_list_sorted[num_timesteps_idx]):,}")
 
     # Visualization is slow, so we'll also save frames to make a GIF later.
     # Set the path for these frames here. 
-    frame_path = os.path.join(output_dir, epochs_list_sorted[epoch_idx][:-4])
+    frame_path = os.path.join(output_dir, num_timesteps_list_sorted[num_timesteps_idx][:-4])
     if not os.path.exists(frame_path):
         os.makedirs(frame_path)
 
@@ -192,7 +192,7 @@ for (k, epoch_idx) in enumerate(epoch_idxs):  # For each epoch index...
 
     # Plot position vs time. 
     fig_pos, ax_pos = plt.subplots(nrows=3, ncols=1, num="Position vs Time")
-    fig_pos.suptitle(f"Model: PPO/{models_available[model_idx]}, Epoch: {extract_number(epochs_list_sorted[epoch_idx]):,}")
+    fig_pos.suptitle(f"Model: PPO/{models_available[model_idx]}, Num Timesteps: {extract_number(num_timesteps_list_sorted[num_timesteps_idx]):,}")
     ax_pos[0].plot(T, x[:, 0], 'b-', linewidth=1, label="RL")
     ax_pos[0].plot(T, x[:, 1:-1], 'b-', linewidth=1)
     ax_pos[0].plot(T, x[:, -1], 'k-', linewidth=2, label="GC")
@@ -214,4 +214,4 @@ for (k, epoch_idx) in enumerate(epoch_idxs):  # For each epoch index...
     # Save fig. 
     fig_pos.savefig(os.path.join(frame_path, 'position_vs_time.png'))
 
-plt.show()
\ No newline at end of file
+plt.show()