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mental breakdown

master
Ruben-FreddyLoafers 2025-11-20 15:32:28 +01:00
parent 6f7dcb8326
commit 6c9a096b61
2 changed files with 38 additions and 48 deletions
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43
04_pacman_rl/pacman.py
View File

@ -127,7 +127,6 @@ def main():
ghost = Ghost(COLS - 2, ROWS - 2)
s = (pacman.x, pacman.y, ghost.x, ghost.y) # as a tuple so the state becomes hashable
opposite_action = {0: 1, 1: 0, 2: 3, 3: 2}
q = rl.q_init()
gamma = 0.9
alpha = 0.8
@ -162,38 +161,24 @@ def main():
running = False
# Start of my code
# s_not_terminal = True
# while s_not_terminal:
s_not_terminal = True
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)
move_pacman(pacman, a)
q[s][a] += round(alpha * (r + gamma * max(q[s_new]) - q[s][a]), 2)
q[s_new][opposite_action[a]] += round(alpha * (r + gamma * max(q[s_new]) - q[s][opposite_action[a]]), 2)
# Update Q-values for all states with the same Pacman position (s0, s1)
pacman_s0, pacman_s1 = s_new[0], s_new[1]
for state_key in q:
if state_key[0] == pacman_s0 and state_key[1] == pacman_s1:
# Update this state's Q-values based on the current transition, but only if action is valid
if q[state_key][a] > 0: # Only update if action is not blocked
q[state_key][a] += round(alpha * (r + gamma * max(q[s_new]) - q[state_key][a]), 2)
if q[state_key][opposite_action[a]] > 0: # Only update if opposite action is not blocked
q[state_key][opposite_action[a]] += round(alpha * (r + gamma * max(q[s_new]) - q[state_key][opposite_action[a]]), 2)
s = s_new
print("s_new: " + str(s_new))
print("q[s] after action with manipulated a: " + str(q[s]))
print("q[s_new] after action: " + str(q[s_new]))
print()
if abs(r - q[s_new][a]) < 0.2: # Reward difference is small (convergence)
s_not_terminal = False
# s = s_new
s = (pacman.x, pacman.y, ghost.x, ghost.y)
time.sleep(0.5)
time.sleep(0.2)
gamma *= gamma
move_pacman(pacman, a)
# Draw the labyrinth, pacman, and ghost
draw_labyrinth()
@ -210,3 +195,17 @@ def main():
if __name__ == "__main__":
main()
"""
for state_key in q:
if state_key[0] == s_new[0] and state_key[1] == s_new[1]:
# Update this state's Q-values based on the current transition, but only if action is valid
if q[state_key][a] > 0: # Only update if action is not blocked
q[state_key][a] += round(alpha * (r + gamma * max(q[s_new]) - q[state_key][a]), 2)
if q[state_key][opposite_action[a]] > 0: # Only update if opposite action is not blocked
q[state_key][opposite_action[a]] += round(alpha * (r + gamma * max(q[s_new]) - q[state_key][opposite_action[a]]), 2)
print("s_new: " + str(s_new))
print("q[s] after action with manipulated a: " + str(q[s]))
print("q[s_new] after action: " + str(q[s_new]))
print()
"""

23
04_pacman_rl/reinforcement_learning.py
View File

@ -12,7 +12,7 @@ def q_init():
# Configuration
NUM_ACTIONS = 4
INITIAL_Q_VALUE = 1.0 # Small value for initialization
INITIAL_Q_VALUE = 3.0 # Small value for initialization
# Labyrinth layout
labyrinth = [
@ -145,26 +145,17 @@ def take_action(s, a, labyrinth):
if a == 3: # down
s_new[1] += 1
# consider if there is a point on the field
# r = 2.0 if labyrinth[s_new[1]][s_new[0]] == "." else -5.0
r = -2
# consider new distance between Pacman and Ghost using actual pathfinding
pacman_pos = (s[0], s[1])
ghost_pos = (s[2], s[3])
pacman_pos_new = (s_new[0], s_new[1])
ghost_pos = (s[2], s[3])
distance_new = bfs_distance(pacman_pos_new, ghost_pos, labyrinth)
# Reward based on distance from ghost (closer distance = worse reward)
if distance_new >= 5:
r -= 2.0 # Good reward for being far away
elif distance_new >= 3:
r -= 1.0 # Small reward for being moderately far
elif distance_new <= 2:
r += 5.0 # Large penalty for being adjacent to ghost
elif distance_new == 1:
r += 10.0 # Large penalty for being adjacent to ghost
# 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
# Reward for eating cookies
r += 5.0 if labyrinth[s_new[1]][s_new[0]] == "." else -2.0
# Ensure reward doesn't drop below 0.01
r = max(r, 0.01)