diff --git a/main.py b/main.py
index 36ee202..3af7301 100644
--- a/main.py
+++ b/main.py
@@ -185,17 +185,17 @@ def main():
 
                 print(f"Models saved to {chkpt_path}")
 
-        metrics.plot_learning_curve(score_history, parsed_args.n_agents, figure_path)
+        metrics.plot_learning_curve(score_history, parsed_args.n_agents, figure_path, n_episodes)
 
         metrics.plot_score(score_history, parsed_args.n_agents, figure_path)
 
-        metrics.plot_loss('actor', actor_loss, parsed_args.n_agents, figure_path)
+        metrics.plot_loss('actor', actor_loss, parsed_args.n_agents, figure_path, n_episodes)
 
-        metrics.plot_loss('critic', critic_loss, parsed_args.n_agents, figure_path)
+        metrics.plot_loss('critic', critic_loss, parsed_args.n_agents, figure_path, n_episodes)
 
-        metrics.plot_parameter('entropy', entropy, parsed_args.n_agents, figure_path)
+        metrics.plot_parameter('entropy', entropy, parsed_args.n_agents, figure_path, n_episodes)
 
-        metrics.plot_parameter('advantage', advantage, parsed_args.n_agents, figure_path)
+        metrics.plot_parameter('advantage', advantage, parsed_args.n_agents, figure_path, n_episodes)
 
         metrics.plot_avg_time(time_alive, parsed_args.n_agents, figure_path)    
 
diff --git a/utils/metrics.py b/utils/metrics.py
index d1f206e..11bfc8e 100644
--- a/utils/metrics.py
+++ b/utils/metrics.py
@@ -3,7 +3,7 @@ import numpy as np
 import matplotlib.pyplot as plt
 
 
-def plot_learning_curve(scores, num_players, figure_path, ep_lenght):
+def plot_learning_curve(scores, num_players, figure_path, n_episodes):
 
     plt.figure()
     plt.title("Running Average - Score")
@@ -13,7 +13,7 @@ def plot_learning_curve(scores, num_players, figure_path, ep_lenght):
     for score in scores:
         running_avg = np.zeros(len(score))
         for i in range(len(score)):
-            running_avg[i] = np.mean(score[max(0, i-int(ep_length/10)):(i+1)])
+            running_avg[i] = np.mean(score[max(0, i-int(n_episodes/10)):(i+1)])
         plt.plot(running_avg)
     plt.savefig(os.path.join(figure_path, "avg_score.png"))
     plt.close()
@@ -40,7 +40,7 @@ def plot_score(scores, num_players, figure_path):
     plt.close()
 
 
-def plot_loss(nn_type, losses, num_players, figure_path, ep_length):
+def plot_loss(nn_type, losses, num_players, figure_path, n_episodes):
 
     plt.figure()
     plt.title(f"Running Average - {nn_type.capitalize()} Loss")
@@ -50,13 +50,13 @@ def plot_loss(nn_type, losses, num_players, figure_path, ep_length):
     for loss in losses:
         running_avg = np.zeros(len(loss))
         for i in range(len(loss)):
-            running_avg[i] = np.mean(loss[max(0, i-int(ep_length/10)):(i+1)])
+            running_avg[i] = np.mean(loss[max(0, i-int(n_episodes/10)):(i+1)])
         plt.plot(running_avg)
     plt.savefig(os.path.join(figure_path, f"{nn_type}_loss.png"))
     plt.close()
 
 
-def plot_parameter(name, parameter, num_players, figure_path, ep_length):
+def plot_parameter(name, parameter, num_players, figure_path, n_episodes):
 
     plt.figure()
     plt.title(f"Running Average - {name.capitalize()}")
@@ -66,7 +66,7 @@ def plot_parameter(name, parameter, num_players, figure_path, ep_length):
     for param in parameter:
         running_avg = np.zeros(len(param))
         for i in range(len(param)):
-            running_avg[i] = np.mean(param[max(0, i-int(ep_length/10)):(i+1)])
+            running_avg[i] = np.mean(param[max(0, i-int(n_episodes/10)):(i+1)])
         plt.plot(running_avg)
     plt.savefig(os.path.join(figure_path, f"{name}.png"))
     plt.close()