From 1453fd930a7e2e36fd4ca5f5567f10f6306e5670 Mon Sep 17 00:00:00 2001
From: Ruben Seitz <ruben.seitz@kiteit.de>
Date: Mon, 24 Nov 2025 10:22:34 +0100
Subject: [PATCH] debugging reward system

---
 04_pacman_rl/pacman.py                 | 18 ++++++++++--------
 04_pacman_rl/reinforcement_learning.py |  9 ++++-----
 2 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/04_pacman_rl/pacman.py b/04_pacman_rl/pacman.py
index dedbb2a..bf4d813 100644
--- a/04_pacman_rl/pacman.py
+++ b/04_pacman_rl/pacman.py
@@ -120,6 +120,7 @@ def move_pacman(pacman, a):
 
 # Main game function
 def main():
+    global labyrinth
     clock = pygame.time.Clock()
 
     # Initialize Pacman and Ghost positions
@@ -129,8 +130,8 @@ 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
+    gamma = 0.90
+    alpha = 0.2
 
     # Game loop
     running = True
@@ -163,8 +164,8 @@ def main():
 
         # Start of my code
         s_not_terminal = True
-        labyrinth_copy = labyrinth.copy()
-        a = 0
+        labyrinth_copy = [list(row) for row in labyrinth]  # Create proper deep copy
+        a = None
         while s_not_terminal:
             print("s: " + str(s))
             print("q[s] before action: " + str(q[s]))
@@ -173,7 +174,7 @@ def main():
             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)
+            # 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
 
@@ -184,11 +185,12 @@ def main():
             if s[0] == s[2] and s[1] == s[3]:
                 s_not_terminal = False
             
-            time.sleep(0.2)
+            # time.sleep(0.05)
         
-        labyrinth_copy = []
-        print("NEW LOOP")
+        s = (pacman.x, pacman.y, ghost.x, ghost.y) # as a tuple so the state becomes hashable
+        a = rl.epsilon_greedy(q, s) # 0 = Left; 1 = Right ; 2 = Up ; 3 = Down
         move_pacman(pacman, a)
+        print("NEW LOOP")
 
         # Draw the labyrinth, pacman, and ghost
         draw_labyrinth()
diff --git a/04_pacman_rl/reinforcement_learning.py b/04_pacman_rl/reinforcement_learning.py
index aa6a336..1207f62 100644
--- a/04_pacman_rl/reinforcement_learning.py
+++ b/04_pacman_rl/reinforcement_learning.py
@@ -80,8 +80,8 @@ def epsilon_greedy(q, s, epsilon=0.2):
     a = q[s].index(q_max)
     
     return a
-    
     """
+
     if np.random.random() < epsilon:
         # Explore: choose random action (excluding blocked actions with Q=0)
         valid_actions = [i for i in range(len(q[s])) if q[s][i] > 0]
@@ -100,7 +100,6 @@ def epsilon_greedy(q, s, epsilon=0.2):
             return 0
     """
 
-
 def bfs_distance(start, end, labyrinth):
     """
     Calculate shortest path distance between two points using BFS.
@@ -150,12 +149,12 @@ def take_action(s, a, labyrinth):
     ghost_pos = (s[2], s[3])
     
     distance_new = bfs_distance(pacman_pos_new, ghost_pos, labyrinth)
+    distance_old = bfs_distance((s[0], s[1]), ghost_pos, labyrinth)
     
-    # Reward inversely proportional to distance from ghost (asymptotes to 0)
-    r = 1.0 / (2.0 + distance_new) if distance_new != float('inf') else 0.0
+    r = 0.05 * distance_new if distance_new != float('inf') else 0.0
     
     # Reward for eating cookies
-    r += 0.5 if labyrinth[s_new[1]][s_new[0]] == "." else -0.5
+    r += 1.0 if labyrinth[s_new[1]][s_new[0]] == "." else -1.5
     
     # Ensure reward doesn't drop below 0.01
     r = max(r, 0.01)