From 48a351518d716994aab918fc35f88d5ded224874 Mon Sep 17 00:00:00 2001
From: Ruben Seitz <ruben.seitz@kiteit.de>
Date: Thu, 27 Nov 2025 16:09:47 +0100
Subject: [PATCH] good enough i guess

---
 04_pacman_rl/reinforcement_learning.py | 58 +++++++++++++-------------
 1 file changed, 28 insertions(+), 30 deletions(-)

diff --git a/04_pacman_rl/reinforcement_learning.py b/04_pacman_rl/reinforcement_learning.py
index 279fcb1..a747b91 100644
--- a/04_pacman_rl/reinforcement_learning.py
+++ b/04_pacman_rl/reinforcement_learning.py
@@ -72,43 +72,41 @@ def q_init():
     # print(list(q_table.items())[:5]) # Uncomment to see the first 5 entries
     return q_table
 
-def epsilon_greedy(q, s, epsilon=0.1):
+def epsilon_greedy(q, s, epsilon=0.025):
     """
     Return which direction Pacman should move to using epsilon-greedy algorithm
     With probability epsilon, choose a random action. Otherwise choose the greedy action.
     Avoids actions that would result in collision with ghost.
     """
-    # 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] is not None]
-    #     if valid_actions:
-    #         return np.random.choice(valid_actions)
-    #     else:
-    #         return np.random.randint(0, len(q[s]))
-    # else:
-    # Get all valid (non-blocked) actions with their Q-values
-    valid_actions = [(i, q[s][i]) for i in range(len(q[s])) if q[s][i] is not None]
-    
-    # Sort by Q-value in descending order
-    valid_actions.sort(key=lambda x: x[1], reverse=True)
-    
-    # Try each action starting from highest Q-value
-    for a, q_val in valid_actions:
-        s_test = list(s)
-        if a == 0:  # left
-            s_test[0] -= 1
-        elif a == 1:  # right
-            s_test[0] += 1
-        elif a == 2:  # up
-            s_test[1] -= 1
-        elif a == 3:  # down
-            s_test[1] += 1
+    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] is not None]
+        return np.random.choice(valid_actions)
         
-        # Check if this action would cause collision
-        if s_test[0] == s[2] and s_test[1] == s[3]:
-            continue  # Skip this action, try next highest Q-value
+    else:
+        # Get all valid (non-blocked) actions with their Q-values
+        valid_actions = [(i, q[s][i]) for i in range(len(q[s])) if q[s][i] is not None]
         
-        return a    
+        # Sort by Q-value in descending order
+        valid_actions.sort(key=lambda x: x[1], reverse=True)
+        
+        # Try each action starting from highest Q-value
+        for a, q_val in valid_actions:
+            s_test = list(s)
+            if a == 0:  # left
+                s_test[0] -= 1
+            elif a == 1:  # right
+                s_test[0] += 1
+            elif a == 2:  # up
+                s_test[1] -= 1
+            elif a == 3:  # down
+                s_test[1] += 1
+            
+            # Check if this action would cause collision
+            if s_test[0] == s[2] and s_test[1] == s[3]:
+                continue  # Skip this action, try next highest Q-value
+            
+            return a    
 
 def max_q(q, s_new, labyrinth, depth=0, max_depth=2):
     """Calculate Q-values for all possible actions in state s_new and return the maximum"""