def init_ale(self, display=False):
self.ale = ALEInterface()
self.ale.setInt(b'random_seed', 123)
# self.ale.setInt(b'delay_msec', 0)
self.ale.setFloat(b'repeat_action_probability', 0.0)
self.ale.setInt(b'frame_skip', self.hyperparams['frame_skip'])
if display:
self.ale.setBool(b'display_screen', True)
self.ale.loadROM(str.encode(self.rom_fnm))
def init_ale(self, display=False):
self.ale = ALEInterface()
self.ale.setInt(b'random_seed', random.randrange(999))
# self.ale.setInt(b'delay_msec', 0)
self.ale.setFloat(b'repeat_action_probability', 0.0)
self.ale.setInt(b'frame_skip', self.hyperparams['frame_skip'])
if display:
self.ale.setBool(b'display_screen', True)
self.ale.loadROM(str.encode(self.rom_fnm))
def __init__(self, args, outputDir):
self.outputDir = outputDir
self.screenCaptureFrequency = args.screen_capture_freq
self.ale = ALEInterface()
self.ale.setBool(b'sound', False)
self.ale.setBool(b'display_screen', False)
self.ale.setInt(b'random_seed', 123456)
random.seed(123)
# Fix https://groups.google.com/forum/#!topic/deep-q-learning/p4FAIaabwlo
self.ale.setFloat(b'repeat_action_probability', 0.0)
self.ale.loadROM(b'./breakout.bin')
self.actionSet = self.ale.getMinimalActionSet()
self.gameNumber = 0
self.stepNumber = 0
self.resetGame()
class AsyncAtariEnvironment(object):
def __init__(self, hyperparams, rom_fnm):
self.hyperparams = hyperparams
self.show_screen = hyperparams['show_screen']
self.state_len = hyperparams['state_len']
# self.screen_resize = hyperparams['screen_resize']
self.rom_fnm = rom_fnm
self.init_ale(display=self.show_screen)
self.actions = self.ale.getMinimalActionSet()
print('Num possible actions: %d' % len(self.actions))
self.state_shape = (84, 84, self.state_len)
def init_ale(self, display=False):
self.ale = ALEInterface()
self.ale.setInt(b'random_seed', 123)
# self.ale.setInt(b'delay_msec', 0)
self.ale.setFloat(b'repeat_action_probability', 0.0)
self.ale.setInt(b'frame_skip', self.hyperparams['frame_skip'])
if display:
self.ale.setBool(b'display_screen', True)
self.ale.loadROM(str.encode(self.rom_fnm))
# def set_screen_shape(self):
# self.start_episode()
# # self.perform_action(0)
# self.state_shape = self.get_state().shape
# print(self.state_shape)
# self.prev_screen_rgb = self.ale.getScreenRGB()
# screen = self.ale.getScreenRGB()
# screen = self.preprocess_screen(screen)
# self.screen_shape = screen.shape
# self.state_shape = (self.state_len,) + \
# self.screen_shape
def preprocess_screen(self, screen_rgb):
# observation = cv2.cvtColor(cv2.resize(
# screen_rgb, (84, 110)), cv2.COLOR_BGR2GRAY)
# observation = observation[26:110, :]
# ret, observation = cv2.threshold(
# observation, 1, 255, cv2.THRESH_BINARY)
# return np.reshape(observation, (84, 84))
screen = np.dot(screen_rgb, np.array([.299, .587,
.114])).astype(np.uint8)
screen = ndimage.zoom(screen, (0.4, 0.525))
screen.resize((84, 84))
# screen = screen / 255.0
return np.array(screen)
def get_state_shape(self):
return self.state_shape
def start_episode(self):
self.ale.reset_game()
self.episode_reward = 0
self.states = []
for _ in range(self.state_len):
self.states.append(self.preprocess_screen(self.ale.getScreenRGB()))
def get_episode_reward(self):
return self.episode_reward
def get_state(self):
curr_state = np.stack(self.states, axis=2)
return curr_state
# curr_state = np.zeros(self.state_shape)
# for i in range(self.state_len):
# curr_state[i] = self.states[i]
# return curr_state
def perform_action(self, action_dist):
# print(action_ind)
action = self.actions[np.argmax(action_dist)]
# self.curr_reward = 0
# for frame in range(self.hyperparams['frame_skip']):
# self.prev_screen = self.ale.getScreenRGB()
# self.curr_reward += self.ale.act(action)
self.curr_reward = self.ale.act(action)
self.episode_reward += self.curr_reward
# if not self.curr_reward == 0:
# print(bcolors.WARNING +
# 'Got real reward!' +
# bcolors.ENDC)
if self.curr_reward > 0:
self.curr_reward = 1
# print(bcolors.WARNING +
# 'Got real reward!' +
# bcolors.ENDC)
elif self.curr_reward < 0:
self.curr_reward = -1
screen = self.ale.getScreenRGB()
# screen = np.maximum(screen, self.prev_screen)
screen = self.preprocess_screen(screen)
self.states = self.states[:self.state_len - 1]
self.states.insert(0, screen)
def start_eval_mode(self):
# self.init_ale(display=self.show_screen)
pass
def end_eval_mode(self):
# self.init_ale(display=False)
pass
def get_reward(self):
return self.curr_reward
def episode_is_over(self):
return self.ale.game_over()
def get_actions(self):
return list(range(len(self.actions)))
class AtariTask(object):
def __init__(self, hyperparams, rom_fnm):
self.hyperparams = hyperparams
self.show_screen = hyperparams['show_screen']
self.state_len = hyperparams['state_len']
# self.screen_resize = hyperparams['screen_resize']
self.rom_fnm = rom_fnm
self.init_ale(display=self.show_screen)
self.actions = self.ale.getMinimalActionSet()
print('Num possible actions: %d' % len(self.actions))
self.state_shape = (84, 84, self.state_len)
def init_ale(self, display=False):
self.ale = ALEInterface()
self.ale.setInt(b'random_seed', random.randrange(999))
# self.ale.setInt(b'delay_msec', 0)
self.ale.setFloat(b'repeat_action_probability', 0.0)
self.ale.setInt(b'frame_skip', self.hyperparams['frame_skip'])
if display:
self.ale.setBool(b'display_screen', True)
self.ale.loadROM(str.encode(self.rom_fnm))
# def set_screen_shape(self):
# self.start_episode()
# # self.perform_action(0)
# self.state_shape = self.get_state().shape
# print(self.state_shape)
# self.prev_screen_rgb = self.ale.getScreenRGB()
# screen = self.ale.getScreenRGB()
# screen = self.preprocess_screen(screen)
# self.screen_shape = screen.shape
# self.state_shape = (self.state_len,) + \
# self.screen_shape
def preprocess_screen(self, screen_rgb):
screen = cv2.cvtColor(cv2.resize(
screen_rgb, (84, 110)), cv2.COLOR_BGR2GRAY)
# observation = observation[26:110, :]
# ret, observation = cv2.threshold(
# observation, 1, 255, cv2.THRESH_BINARY)
# return np.reshape(observation, (84, 84))
# screen = np.dot(
# screen_rgb, np.array([.299, .587, .114])).astype(np.uint8)
screen = ndimage.zoom(screen_rgb, (0.4, 0.525))
screen.resize((84, 84))
# screen = screen / 255.0
return np.array(screen)
def get_state_shape(self):
return self.state_shape
def get_actions(self):
return list(range(len(self.actions)))
def start_episode(self):
self.ale.reset_game()
self.episode_reward = 0
self.states = []
for _ in range(self.state_len):
self.states.append(self.preprocess_screen(
self.ale.getScreenRGB()))
def get_episode_reward(self):
return self.episode_reward
def get_state(self):
curr_state = np.stack(self.states, axis=2)
return curr_state
# curr_state = np.zeros(self.state_shape)
# for i in range(self.state_len):
# curr_state[i] = self.states[i]
# return curr_state
def perform_action(self, action_dist):
# print(action_ind)
action = self.actions[np.argmax(action_dist)]
# self.curr_reward = 0
# for frame in range(self.hyperparams['frame_skip']):
# self.prev_screen = self.ale.getScreenRGB()
# self.curr_reward += self.ale.act(action)
self.curr_reward = self.ale.act(action)
self.episode_reward += self.curr_reward
# if not self.curr_reward == 0:
# print(bcolors.WARNING +
# 'Got real reward!' +
# bcolors.ENDC)
if self.curr_reward > 0:
self.curr_reward = 1
# print(bcolors.WARNING +
# 'Got real reward!' +
# bcolors.ENDC)
elif self.curr_reward < 0:
self.curr_reward = -1
screen = self.ale.getScreenRGB()
# screen = np.maximum(screen, self.prev_screen)
screen = self.preprocess_screen(screen)
self.states = self.states[:self.state_len - 1]
self.states.insert(0, screen)
def start_eval_mode(self):
# self.init_ale(display=self.show_screen)
pass
def end_eval_mode(self):
# self.init_ale(display=False)
pass
def get_reward(self):
return self.curr_reward
def episode_is_over(self):
return self.ale.game_over()
from ale_python_interface.ale_python_interface import ALEInterface
import numpy as np
# import pygame
ale = ALEInterface()
ale.setInt(b"random_seed", 123)
ale.setBool(b'display_screen', True)
ale.loadROM(str.encode('data/roms/breakout.bin'))
random_seed = ale.getInt(b"random_seed")
print("random_seed: " + str(random_seed))
legal_actions = ale.getMinimalActionSet()
(screen_width, screen_height) = ale.getScreenDims()
print("width/height: " + str(screen_width) + "/" + str(screen_height))
# init pygame
# pygame.init()
# print(ale.getScreenGrayscale().flatten().shape)
# screen = pygame.display.set_mode((160, 210))
# pygame.display.set_caption("Arcade Learning Environment Random Agent Display")
# pygame.display.flip()
episode = 0
total_reward = 0.0
while(episode < 10):
a = legal_actions[np.random.randint(legal_actions.size)]
reward = ale.act(a)
from ale_python_interface.ale_python_interface import ALEInterface
import numpy as np
# import pygame
ale = ALEInterface()
ale.setInt(b"random_seed", 123)
ale.setBool(b'display_screen', True)
ale.loadROM(str.encode('data/roms/breakout.bin'))
random_seed = ale.getInt(b"random_seed")
print("random_seed: " + str(random_seed))
legal_actions = ale.getMinimalActionSet()
(screen_width, screen_height) = ale.getScreenDims()
print("width/height: " + str(screen_width) + "/" + str(screen_height))
# init pygame
# pygame.init()
# print(ale.getScreenGrayscale().flatten().shape)
# screen = pygame.display.set_mode((160, 210))
# pygame.display.set_caption("Arcade Learning Environment Random Agent Display")
# pygame.display.flip()
episode = 0
total_reward = 0.0
while (episode < 10):
a = legal_actions[np.random.randint(legal_actions.size)]
reward = ale.act(a)
class AtariEnvironment:
def __init__(self, args, outputDir):
self.outputDir = outputDir
self.screenCaptureFrequency = args.screen_capture_freq
self.ale = ALEInterface()
self.ale.setBool(b'sound', False)
self.ale.setBool(b'display_screen', False)
self.ale.setInt(b'random_seed', 123456)
random.seed(123)
# Fix https://groups.google.com/forum/#!topic/deep-q-learning/p4FAIaabwlo
self.ale.setFloat(b'repeat_action_probability', 0.0)
self.ale.loadROM(b'./breakout.bin')
self.actionSet = self.ale.getMinimalActionSet()
self.gameNumber = 0
self.stepNumber = 0
self.resetGame()
def getNumActions(self):
return len(self.actionSet)
def getState(self):
return self.state
def getGameNumber(self):
return self.gameNumber
def getFrameNumber(self):
return self.ale.getFrameNumber()
def getEpisodeFrameNumber(self):
return self.ale.getEpisodeFrameNumber()
def getEpisodeStepNumber(self):
return self.episodeStepNumber
def getStepNumber(self):
return self.stepNumber
def getGameScore(self):
return self.gameScore
def isGameOver(self):
return self.ale.game_over()
def step(self, action):
previousLives = self.ale.lives()
reward = 0
isTerminal = 0
self.stepNumber += 1
self.episodeStepNumber += 1
for i in range(4):
prevScreenRGB = self.ale.getScreenRGB()
reward += self.ale.act(self.actionSet[action])
screenRGB = self.ale.getScreenRGB()
# Detect end of episode, I don't think I'm handling this right in terms
# of the overall game loop (??)
if self.ale.lives() < previousLives or self.ale.game_over():
isTerminal = 1
break
if self.gameNumber % self.screenCaptureFrequency == 0:
dir = self.outputDir + '/screen_cap/game-%06d' % (self.gameNumber)
if not os.path.isdir(dir):
os.makedirs(dir)
#self.ale.saveScreenPNG(dir + '/frame-%06d.png' % (self.getEpisodeFrameNumber()))
maxedScreen = np.maximum(screenRGB, prevScreenRGB)
self.state = self.state.stateByAddingScreen(maxedScreen, self.ale.getFrameNumber())
self.gameScore += reward
return reward, self.state, isTerminal
def resetGame(self):
if self.ale.game_over():
self.gameNumber += 1
self.ale.reset_game()
self.state = State().stateByAddingScreen(self.ale.getScreenRGB(), self.ale.getFrameNumber())
self.gameScore = 0
self.episodeStepNumber = 0 # environment steps vs ALE frames. Will probably be 4*frame number