diff --git a/04_pacman_rl/pacman.py b/04_pacman_rl/pacman.py
index 7cc3f73..dedbb2a 100644
--- a/04_pacman_rl/pacman.py
+++ b/04_pacman_rl/pacman.py
@@ -128,6 +128,7 @@ def main():
 
     s = (pacman.x, pacman.y, ghost.x, ghost.y) # as a tuple so the state becomes hashable
     q = rl.q_init()
+    a_opposite_direction = {0: 1, 1: 0, 2: 3, 3: 2}
     gamma = 0.9
     alpha = 0.8
 
@@ -162,22 +163,31 @@ def main():
 
         # Start of my code
         s_not_terminal = True
+        labyrinth_copy = labyrinth.copy()
+        a = 0
         while s_not_terminal:
             print("s: " + str(s))
             print("q[s] before action: " + str(q[s]))
 
             a = rl.epsilon_greedy(q, s) # 0 = Left; 1 = Right ; 2 = Up ; 3 = Down
-            s_new, r = rl.take_action(s, a, labyrinth)
+            s_new, r, labyrinth_copy = rl.take_action(s, a, labyrinth_copy)
             
             q[s][a] += round(alpha * (r + gamma * max(q[s_new]) - q[s][a]), 2)
+            q[s_new][a_opposite_direction[a]] += round(alpha * (r + gamma * max(q[s]) - q[s_new][a_opposite_direction[a]]), 2)
             
             s = s_new
+
+            if all("." not in row for row in labyrinth_copy):
+                s_not_terminal = False
             
-            if abs(r - q[s_new][a]) < 0.2:  # Reward difference is small (convergence)
+            # Check for collisions (game over if ghost catches pacman)
+            if s[0] == s[2] and s[1] == s[3]:
                 s_not_terminal = False
             
             time.sleep(0.2)
         
+        labyrinth_copy = []
+        print("NEW LOOP")
         move_pacman(pacman, a)
 
         # Draw the labyrinth, pacman, and ghost
diff --git a/04_pacman_rl/reinforcement_learning.py b/04_pacman_rl/reinforcement_learning.py
index c9b37e4..aa6a336 100644
--- a/04_pacman_rl/reinforcement_learning.py
+++ b/04_pacman_rl/reinforcement_learning.py
@@ -12,7 +12,7 @@ def q_init():
 
     # Configuration
     NUM_ACTIONS = 4
-    INITIAL_Q_VALUE = 3.0 # Small value for initialization
+    INITIAL_Q_VALUE = 2.0 # Small value for initialization
 
     # Labyrinth layout
     labyrinth = [
@@ -43,8 +43,6 @@ def q_init():
                         continue
                     if s3 == 2 and s2 not in s_constrained_values:
                         continue
-                    if s0 == s2 and s1 == s3:
-                        continue
 
                     # Assign all possible states a tuple of values
                     state_key = (s0, s1, s2, s3)
@@ -135,6 +133,8 @@ def bfs_distance(start, end, labyrinth):
 
 
 def take_action(s, a, labyrinth):
+    labyrinth_copy = [list(row) for row in labyrinth]
+    labyrinth_copy[s[1]][s[0]] = " "
     s_new = list(s)
     if a == 0:  # left
         s_new[0] -= 1
@@ -152,12 +152,12 @@ def take_action(s, a, labyrinth):
     distance_new = bfs_distance(pacman_pos_new, ghost_pos, labyrinth)
     
     # Reward inversely proportional to distance from ghost (asymptotes to 0)
-    r = 1.0 / (1.0 + distance_new) if distance_new != float('inf') else 0.0
+    r = 1.0 / (2.0 + distance_new) if distance_new != float('inf') else 0.0
     
     # Reward for eating cookies
-    r += 5.0 if labyrinth[s_new[1]][s_new[0]] == "." else -2.0
+    r += 0.5 if labyrinth[s_new[1]][s_new[0]] == "." else -0.5
     
     # Ensure reward doesn't drop below 0.01
     r = max(r, 0.01)
     
-    return tuple(s_new), r
\ No newline at end of file
+    return tuple(s_new), r, labyrinth_copy
\ No newline at end of file