diff --git a/argument_swapper.py b/argument_swapper.py
new file mode 100644
index 0000000..9dbd365
--- /dev/null
+++ b/argument_swapper.py
@@ -0,0 +1,11 @@
+import numpy as np
+from learning import runTry
+
+EPSILON = 0.5
+ALPHA = 0.5
+GAMMA = 0.5
+
+STEPS = 10
+
+for alpha in np.linspace(0.1, 0.5, 10):
+    runTry(EPSILON, alpha, GAMMA)
\ No newline at end of file
diff --git a/learning.py b/learning.py
new file mode 100644
index 0000000..0719915
--- /dev/null
+++ b/learning.py
@@ -0,0 +1,58 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from game import run_game
+from util import initial_q_fill
+
+
+EPSILON = 0.5
+ALPHA = 0.3
+GAMMA = 0.8
+
+def runTry(EPSILON, ALPHA, GAMMA):
+    """
+    state: (x_distance_to_ghost, y_distance_to_ghost, next_cookie_Direction)
+    action: Direction
+    q_value: (state, action)
+    """
+
+    AMOUNT_RUNS = 5000
+    q_values = {}
+
+    initial_q_fill(q_values)
+
+    cookies_per_run = []
+    # Amount of single runs
+    for x in range(AMOUNT_RUNS):
+        amount_cookies_ate = run_game(q_values, EPSILON, ALPHA, GAMMA)
+        cookies_per_run.append(amount_cookies_ate)
+        # print(f"Run {x}: {amount_cookies_ate} cookies ate\n")
+
+    wins = 0
+    for element in cookies_per_run:
+        if element == 20:
+            wins += 1
+        
+    print(f"Win percentage: {wins/AMOUNT_RUNS}%")
+
+    return cookies_per_run
+
+
+
+cookies_per_run = runTry(EPSILON, ALPHA, GAMMA)
+
+
+window_size = 100  # Adjust based on your needs
+rolling_avg = pd.Series(cookies_per_run).rolling(window=window_size, center=True).mean()
+
+plt.figure(figsize=(12, 6))
+plt.plot(cookies_per_run, alpha=0.2, label='Raw Data', linewidth=0.5, color='gray')
+plt.plot(rolling_avg, label=f'{window_size}-point Moving Average', 
+            linewidth=2, color='blue')
+plt.title("Data with Rolling Average")
+plt.xlabel("Index")
+plt.ylabel("Value")
+plt.legend()
+plt.grid(True, alpha=0.3)
+plt.show()
+
diff --git a/main.py b/main.py
deleted file mode 100644
index 182dd0d..0000000
--- a/main.py
+++ /dev/null
@@ -1,50 +0,0 @@
-import matplotlib.pyplot as plt
-import pandas as pd
-
-from game import run_game
-from util import initial_q_fill
-
-
-AMOUNT_RUNS = 5000
-EPSILON = 0.5
-ALPHA = 0.3
-GAMMA = 0.5
-
-
-"""
-state: (x_distance_to_ghost, y_distance_to_ghost, next_cookie_Direction)
-action: Direction
-q_value: (state, action)
-"""
-q_values = {}
-initial_q_fill(q_values)
-
-cookies_per_run = []
-# Amount of single runs
-for x in range(AMOUNT_RUNS):
-    amount_cookies_ate = run_game(q_values, EPSILON, ALPHA, GAMMA)
-    cookies_per_run.append(amount_cookies_ate)
-    # print(f"Run {x}: {amount_cookies_ate} cookies ate\n")
-
-wins = 0
-for element in cookies_per_run:
-    if element == 20:
-        wins += 1
-    
-print(f"Win percentage: {wins/AMOUNT_RUNS}%")
-
-
-window_size = 100  # Adjust based on your needs
-rolling_avg = pd.Series(cookies_per_run).rolling(window=window_size, center=True).mean()
-
-plt.figure(figsize=(12, 6))
-plt.plot(cookies_per_run, alpha=0.2, label='Raw Data', linewidth=0.5, color='gray')
-plt.plot(rolling_avg, label=f'{window_size}-point Moving Average', 
-            linewidth=2, color='blue')
-plt.title("Data with Rolling Average")
-plt.xlabel("Index")
-plt.ylabel("Value")
-plt.legend()
-plt.grid(True, alpha=0.3)
-plt.show()
-