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MLE-Pacman
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it finally works?

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2wenty1ne 2025-12-08 13:28:25 +01:00
parent cfdef49a73
commit 0f98ce944e
3 changed files with 98 additions and 40 deletions
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57
ReinforcmentLearning/game.py
View File

@ -12,8 +12,6 @@ SCREEN_WIDTH = 400
SCREEN_HEIGHT = 400
CELL_SIZE = 40
os.environ['SDL_VIDEODRIVER'] = 'dummy'
# Define colors
YELLOW = (255, 255, 0)
RED = (255, 0, 0)
@ -21,6 +19,8 @@ WHITE = (255, 255, 255)
BLUE = (0, 0, 255)
BLACK = (0, 0, 0)
REWARD_ON_HALF = 50
# Labyrinth as a string
labyrinth_init = [
"##########",
@ -37,9 +37,9 @@ ROWS = len(labyrinth)
COLS = len(labyrinth[0])
# Initialize game screen
# screen = pygame.display.set_mode((COLS * CELL_SIZE, ROWS * CELL_SIZE))
# pygame.display.set_caption("Micro-Pacman")
screen = None
# screen = pygame.Surface((COLS * CELL_SIZE, ROWS * CELL_SIZE))
# pygame.display.set_caption("Micro-Pacman")
# Pacman class
class Pacman:
@ -115,24 +115,34 @@ def draw_labyrinth(labyrinth):
# Main game function
def run_game(q_values, EPSILON, ALPHA, GAMMA, REWARD_ON_WIN, REWARD_ON_LOSE):
def run_game(q_values, EPSILON, ALPHA, GAMMA, REWARD_ON_WIN, REWARD_ON_LOSE, show_game):
clock = pygame.time.Clock()
labyrinth = labyrinth_init.copy()
if show_game:
pygame.display.set_caption("Micro-Pacman")
screen = pygame.display.set_mode((COLS * CELL_SIZE, ROWS * CELL_SIZE))
# Initialize Pacman and Ghost positions
pacman = Pacman(1, 1)
ghost = Ghost(COLS - 2, ROWS - 2)
#? -------------------------MY CODE-----------------------------------
state = calc_current_state(labyrinth, pacman.x, pacman.y, ghost.x, ghost.y)
reward = 0
#? -------------------------MY CODE-----------------------------------
#? GAME LOOP
running = True
iter = 0
while running:
#screen.fill(BLACK)
#? -------------------------MY CODE-----------------------------------
reward = 0
#? -------------------------MY CODE-----------------------------------
if show_game:
screen.fill(BLACK)
iter = iter + 1
# Handle events
for event in pygame.event.get():
@ -176,9 +186,25 @@ def run_game(q_values, EPSILON, ALPHA, GAMMA, REWARD_ON_WIN, REWARD_ON_LOSE):
if labyrinth[pacman.y][pacman.x] == ".":
labyrinth[pacman.y] = labyrinth[pacman.y][:pacman.x] + " " + labyrinth[pacman.y][pacman.x+1:]
#? -------------------------MY CODE-----------------------------------
cookie_counter = 0
for y, row in enumerate(labyrinth):
for x, cell in enumerate(row):
if cell == ".":
cookie_counter += 1
if cookie_counter == 10:
reward = REWARD_ON_HALF
if show_game:
print("Half reward")
#? -------------------------MY CODE-----------------------------------
# Check if all cookies are eaten (game over)
if all("." not in row for row in labyrinth):
# print("You Win! Pacman ate all the cookies.")
if show_game:
print("You Win! Pacman ate all the cookies.")
reward = REWARD_ON_WIN
running = False
@ -208,15 +234,16 @@ def run_game(q_values, EPSILON, ALPHA, GAMMA, REWARD_ON_WIN, REWARD_ON_LOSE):
return 20-counter
#? -------------------------MY CODE-----------------------------------
#draw_labyrinth(labyrinth)
#pacman.draw()
#ghost.draw()
if show_game:
draw_labyrinth(labyrinth)
pacman.draw()
ghost.draw()
# Update display
# pygame.display.flip()
# Update display
pygame.display.flip()
# Cap the frame rate
# clock.tick(5)
# Cap the frame rate
clock.tick(20)
pygame.quit()

64
ReinforcmentLearning/learning.py
View File

@ -12,7 +12,8 @@ def multipleTries(EPSILON, ALPHA, GAMMA, AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WI
wins_per_try = []
for x in range(AMOUNT_TRIES):
cookies_per_run, amount_wins = oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE)
plot_result = False
cookies_per_run, amount_wins = oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE, plot_result)
cookies_per_run.append(cookies_per_run)
wins_per_try.append(amount_wins)
# print(f"Finished try {x+1}\n")
@ -20,7 +21,7 @@ def multipleTries(EPSILON, ALPHA, GAMMA, AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WI
return cookies_per_try, wins_per_try
def oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE):
def oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE, plot_result, show_game):
"""
state: (x_distance_to_ghost, y_distance_to_ghost, next_cookie_Direction)
action: Direction
@ -34,29 +35,54 @@ def oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE):
cookies_per_run = []
# Amount of single runs
for x in range(AMOUNT_RUNS):
amount_cookies_ate = run_game(q_values, EPSILON, ALPHA, GAMMA, REWARD_ON_WIN, REWARD_ON_LOSE)
amount_cookies_ate = run_game(q_values, EPSILON, ALPHA, GAMMA, REWARD_ON_WIN, REWARD_ON_LOSE, show_game)
cookies_per_run.append(amount_cookies_ate)
wins = 0
for element in cookies_per_run:
if element == 20:
wins += 1
toAdd = 1 if element == 20 else 0
wins = sum(1 for result in cookies_per_run if result == 20)
print(f"Win percentage: {(wins/AMOUNT_RUNS)*100}%")
if plot_result:
plot_results(cookies_per_run)
return cookies_per_run, wins
def print_results(cookies_per_try, wins_per_try, EPSILON, ALPHA, GAMMA, AMOUNT_RUNS):
# print("---------DONE---------")
# print("Used: ")
# print(f"Epsilon: {EPSILON}")
# print(f"Gamma: {GAMMA}")
# print(f"Alpha: {ALPHA}")
def plot_results(cookies_per_run):
wins = []
losses = []
win_count = 0
# print("---------SUMMARY---------")
print(f"Average win percantage: {((sum(wins_per_try) / len(wins_per_try)) / AMOUNT_RUNS)*100}%\n")
# print(f"Best try: {(max(wins_per_try) / AMOUNT_RUNS)*100}%")
# print(f"Worst try: {(min(wins_per_try) / AMOUNT_RUNS)*100}%")
for i, r in enumerate(cookies_per_run):
if r == 20:
win_count += 1
wins.append(win_count)
losses.append((i + 1) - win_count) # Losses count down from top
# Last 700 attempts
last_700_wins = wins[-700:] if len(wins) >= 700 else wins
last_700_losses = losses[-700:] if len(losses) >= 700 else losses
last_700_indices = list(range(len(wins)-len(last_700_wins)+1, len(wins)+1))
# Create figure with 2 subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
# Plot 1: All attempts (with thicker lines: linewidth=1.5)
ax1.plot(range(1, len(wins)+1), wins, 'b-', linewidth=1.5, label='Wins')
ax1.plot(range(1, len(losses)+1), losses, 'orange', linewidth=1.5, label='Losses')
ax1.set_xlabel('Attempt')
ax1.set_ylabel('Count')
ax1.set_title('All Attempts: Wins vs Losses')
ax1.legend()
# Plot 2: Last 700 attempts (with thicker lines: linewidth=1.5)
ax2.plot(last_700_indices, last_700_wins, 'b-', linewidth=1.5, label='Wins')
ax2.plot(last_700_indices, last_700_losses, 'orange', linewidth=1.5, label='Losses')
ax2.set_xlabel('Attempt')
ax2.set_ylabel('Count')
ax2.set_title(f'Last {len(last_700_wins)} Attempts: Wins vs Losses')
ax2.legend()
plt.tight_layout()
plt.show()

17
main.py
View File

@ -1,17 +1,22 @@
from GenTunic.gen_tuning import gen_tuning_main
from ReinforcmentLearning.learning import multipleTries
from ReinforcmentLearning.learning import multipleTries, oneTry
EPSILON = 0.1
ALPHA = 0.4
GAMMA = 0.8
# EPSILON = 0.1618
EPSILON = 0.01
ALPHA = 0.01
GAMMA = 0.2713
AMOUNT_RUNS = 5000
AMOUNT_TRIES = 5
REWARD_ON_WIN = 10
REWARD_ON_WIN = 100
REWARD_ON_LOSE = -450
plot_result = True
show_game = False
multipleTries(EPSILON, ALPHA, GAMMA,AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE)
oneTry(EPSILON, ALPHA, GAMMA, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE, plot_result, show_game)
#multipleTries(EPSILON, ALPHA, GAMMA,AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE)
#gen_tuning_main(AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE)