def agent_start(self, observation): screen = observation.intArray[128:] screen = np.reshape(screen, (210, -1)) maze = detect_maze(screen) self.image = pacman_image(maze) return_action = Action() action = randrange(self.numActions) return_action.intArray = [action] self.lastAction = copy.deepcopy(return_action) self.lastObservation = copy.deepcopy(observation) return return_action
import pygame
pygame.init()
ale.setBool('sound', False) # Sound doesn't work on OSX
elif sys.platform.startswith('linux'):
ale.setBool('sound', True)
ale.setBool('display_screen', True)
# Load the ROM file
ale.loadROM(sys.argv[1])
# Get the list of legal actions
legal_actions = ale.getLegalActionSet()
# Play 10 episodes
screen = np.reshape(ale.getScreen(), (210, -1))
maze = detect_maze(screen)
image = pacman_image(maze)
# print_maze(maze)
for episode in xrange(1):
total_reward = 0
step = 1
while not ale.game_over():
# if step == 500:
screen = np.reshape(ale.getScreen(), (210, -1))
if step % 3 == 0:
image.new_image(screen)
a = legal_actions[randrange(len(legal_actions))]
step += 1
# Apply an action and get the resulting reward
reward = ale.act(a);
