class DoomEnv(gym.Env, EzPickle):
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 35
}
def __init__(self, level='deathmatch', obs_type='ram'):
# super(DoomEnv, self).__init__()
EzPickle.__init__(self, level.split('.')[0], obs_type)
assert obs_type in ('ram', 'image')
level = level.split('.')[0]
Config.init(level)
self.curr_seed = 0
self.game = DoomGame()
self.lock = (DoomLock()).get_lock()
self.level = level
self.obs_type = obs_type
self.tick = 4
self._mode = 'algo'
self.is_render_in_human_mode = True
self.is_game_initialized = False
self.is_level_loaded = False
self.viewer = None
self.set_game(self.level, resolution=None, render=True)
print()
# todo: add frame skip option by using tick
def step(self, action):
reward = 0.0
# self.tick = 4
if self._mode == 'algo':
if self.tick:
reward = self.game.make_action(action, self.tick)
else:
reward = self.game.make_action(action)
# self.game.set_action(action)
# self.game.advance_action(4)
# reward = self.game.get_last_reward()
return self.get_obs(), reward, self.isDone(), self.get_info()
def reset(self):
if not self.is_game_initialized:
self.__load_level()
self.__init_game()
self.__start_episode()
return self.get_obs()
def render(self, mode='human', **kwargs):
if 'close' in kwargs and kwargs['close']:
if self.viewer is not None:
self.viewer.close()
self.viewer = None
return
if mode == 'human' and not self.is_render_in_human_mode:
return
img = self.get_image()
if mode == 'rgb_array':
return img
elif mode is 'human':
if self.viewer is None:
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(img)
def close(self):
with self.lock:
self.game.close()
def seed(self, seed=None):
self.curr_seed = seeding.hash_seed(seed) % 2**32
return [self.curr_seed]
# ================================== GETTERS SETTERS ===============================================================
def set_game(self, level, resolution, render):
self.__configure()
self.__load_level(level)
self.__set_resolution(resolution)
self.__set_obs_and_ac_space()
self.__set_player(render)
def __configure(self, lock=None, **kwargs):
self.seed()
if lock is not None:
self.lock = lock
def __load_level(self, level=None):
if level is not None:
self.level = level.split('.')[0]
self.is_level_loaded = False
if self.is_level_loaded:
return
if self.is_game_initialized:
self.is_game_initialized = False
self.game.close()
self.game = DoomGame()
if not self.is_game_initialized:
self.game.set_vizdoom_path(Config.VIZDOOM_PATH)
self.game.set_doom_game_path(Config.FREEDOOM_PATH)
# Common settings
self.record_file_path = Config.RECORD_FILE_PATH
self.game.load_config(Config.VIZDOOM_SCENARIO_PATH +
Config.DOOM_SETTINGS[self.level][Config.CONFIG])
self.game.set_doom_scenario_path(
Config.VIZDOOM_SCENARIO_PATH +
Config.DOOM_SETTINGS[self.level][Config.SCENARIO])
if Config.DOOM_SETTINGS[self.level][Config.MAP] != '':
self.game.set_doom_map(
Config.DOOM_SETTINGS[self.level][Config.MAP])
self.game.set_doom_skill(
Config.DOOM_SETTINGS[self.level][Config.DIFFICULTY])
self.allowed_actions = Config.DOOM_SETTINGS[self.level][Config.ACTIONS]
self.available_game_variables = Config.DOOM_SETTINGS[self.level][
Config.GAME_VARIABLES]
self.is_level_loaded = True
def __set_resolution(self, resolution=None):
if resolution is None:
resolution = Config.DEFAULT_SCREEN_RESOLUTION
resolution_l = resolution.lower()
if resolution_l not in resolutions:
raise gym.error.Error(
'Error - The specified resolution "{}" is not supported by Vizdoom.\n The list of valid'
'resolutions: {}'.format(resolution, resolutions))
if '_' in resolution_l:
resolution_l = resolution_l.split('_')[1]
self.scr_width = int(resolution_l.split("x")[0])
self.scr_height = int(resolution_l.split("x")[1])
self.game.set_screen_resolution(
getattr(ScreenResolution,
'RES_{}X{}'.format(self.scr_width, self.scr_height)))
self.screen_format = self.game.get_screen_format()
self.screen_height = self.game.get_screen_height()
self.screen_width = self.game.get_screen_width()
def __set_obs_and_ac_space(self):
if self.obs_type == 'ram':
self.observation_space = spaces.Box(
low=0,
high=255,
dtype=np.uint8,
shape=(len(self.available_game_variables), ))
elif self.obs_type == 'image':
# self.observation_space = self.screen_resized
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.scr_height,
self.scr_width, 3),
dtype=np.uint8)
else:
raise error.Error('Unrecognized observation type: {}'.format(
self.obs_type))
if self.screen_format in inverted_screen_formats:
self.dummy_screen = np.zeros(shape=(3, self.scr_height,
self.scr_width),
dtype=np.uint8)
else:
self.dummy_screen = np.zeros(shape=(self.scr_height,
self.scr_width, 3),
dtype=np.uint8)
self.dummy_ram = [0] * len(self.available_game_variables)
self.available_action_codes = [
list(a)
for a in it.product([0, 1],
repeat=self.game.get_available_buttons_size())
]
# self.__delete_conflict_actions()
self.action_space = spaces.MultiDiscrete(
[len(self.available_action_codes)])
def __set_player(self, render=True):
self.game.set_window_visible(render)
self.game.set_mode(Mode.PLAYER)
def __init_game(self):
try:
with self.lock:
self.game.init()
self.is_game_initialized = True
except (ViZDoomUnexpectedExitException, ViZDoomErrorException):
raise error.Error('Could not start the game.')
def __start_episode(self):
if self.curr_seed > 0:
self.game.set_seed(self.curr_seed)
self.curr_seed = 0
if self.record_file_path:
self.game.new_episode(self.record_file_path)
else:
self.game.new_episode()
return
def getState(self):
return self.game.get_state()
def getLastAction(self):
return self.game.get_last_action()
def getButtonsNames(self, action):
return action_to_buttons(self.allowed_actions, action)
def get_info(self):
info = {
"LEVEL": self.level,
"TOTAL_REWARD": round(self.game.get_total_reward(), 4)
}
state_variables = self.get_ram()
for i in range(len(self.available_game_variables)):
info[self.available_game_variables[i]] = state_variables[i]
return info
def get_ram(self):
if not self.is_game_initialized:
raise NotImplementedError(
"The game was not initialized. Run env.reset() first!")
try:
ram = self.getState().game_variables
except AttributeError:
ram = self.dummy_ram
return ram
def get_image(self):
try:
screen = self.getState().screen_buffer.copy()
except AttributeError:
screen = self.dummy_screen
return self.invert_screen(screen)
def get_obs(self):
if self.obs_type == 'ram':
return self.get_ram()
elif self.obs_type == 'image':
return self.get_image()
def isDone(self):
return self.game.is_episode_finished() or self.game.is_player_dead(
) or self.getState() is None
# =========================================== ==============================================================
def invert_screen(self, img):
if self.screen_format in inverted_screen_formats:
return np.rollaxis(img, 0, 3)
else:
return img
def __delete_conflict_actions(self):
if self._mode == 'human':
return
action_codes_copy = self.available_action_codes.copy()
print("Initial actions size: " + str(len(action_codes_copy)))
for i in tqdm.trange(len(self.available_action_codes)):
action = self.available_action_codes[i]
ac_names = action_to_buttons(self.allowed_actions, action)
if all(elem in ac_names
for elem in ['MOVE_LEFT', 'MOVE_RIGHT']) or all(
elem in ac_names
for elem in ['MOVE_BACKWARD', 'MOVE_FORWARD']) or all(
elem in ac_names
for elem in ['TURN_RIGHT', 'TURN_LEFT']) or all(
elem in ac_names for elem in
['SELECT_NEXT_WEAPON', 'SELECT_PREV_WEAPON']):
action_codes_copy.remove(action)
print("Final actions size: " + str(len(action_codes_copy)))
self.available_action_codes = action_codes_copy
def __initHumanPlayer(self):
self._mode = 'human'
self.__load_level()
self.game.add_game_args('+freelook 1')
self.game.set_window_visible(True)
self.game.set_mode(Mode.SPECTATOR)
self.is_render_in_human_mode = False
self.__init_game()
def advanceAction(self, tick=0):
try:
if tick:
self.game.advance_action(tick)
else:
self.game.advance_action()
return True
except ViZDoomUnexpectedExitException:
return False
def playHuman(self):
self.__initHumanPlayer()
while not self.game.is_episode_finished(
) and not self.game.is_player_dead():
self.advanceAction()
state = self.getState()
if state is None:
if self.record_file_path is None:
self.game.new_episode()
else:
self.game.new_episode(self.record_file_path)
state = self.getState()
total_reward = self.game.get_total_reward()
info = self.get_info()
info["TOTAL_REWARD"] = round(total_reward, 4)
print('===============================')
print('State: #' + str(state.number))
print('Action: \t' + str(self.game.get_last_action()) +
'\t (=> only allowed actions)')
print('Reward: \t' + str(self.game.get_last_reward()))
print('Total Reward: \t' + str(total_reward))
print('Variables: \n' + str(info))
sleep(0.02857) # 35 fps = 0.02857 sleep between frames
print('===============================')
print('Done')
return
class VizDoomEnv(Env):
'''
Wrapper for vizdoom to use as an OpenAI gym environment.
'''
metadata = {'render.modes': ['human', 'rgb_array']}
def __init__(self, cfg_name, repeat=1):
super(VizDoomEnv, self).__init__()
self.game = DoomGame()
self.game.load_config('./slm_lab/env/vizdoom/cfgs/' + cfg_name +
'.cfg')
self._viewer = None
self.repeat = 1
# TODO In future, need to update action to handle (continuous) DELTA buttons using gym's Box space
self.action_space = spaces.MultiDiscrete(
[2] * self.game.get_available_buttons_size())
self.action_space.dtype = 'uint8'
output_shape = (self.game.get_screen_height(),
self.game.get_screen_width(),
self.game.get_screen_channels())
self.observation_space = spaces.Box(low=0,
high=255,
shape=output_shape,
dtype='uint8')
self.game.init()
def close(self):
self.game.close()
if self._viewer is not None:
self._viewer.close()
self._viewer = None
def seed(self, seed=None):
self.game.set_seed(seed)
def step(self, action):
reward = self.game.make_action(list(action), self.repeat)
state = self.game.get_state()
done = self.game.is_episode_finished()
# info = self._get_game_variables(state.game_variables)
info = {}
if state is not None:
observation = state.screen_buffer.transpose(1, 2, 0)
else:
observation = np.zeros(shape=self.observation_space.shape,
dtype=np.uint8)
return observation, reward, done, info
def reset(self):
# self.seed(seed)
self.game.new_episode()
return self.game.get_state().screen_buffer.transpose(1, 2, 0)
def render(self, mode='human', close=False):
if close:
if self._viewer is not None:
self._viewer.close()
self._viewer = None
return
img = None
state = self.game.get_state()
if state is not None:
img = state.screen_buffer
if img is None:
# at the end of the episode
img = np.zeros(shape=self.observation_space.shape, dtype=np.uint8)
if mode == 'rgb_array':
return img
elif mode is 'human':
if self._viewer is None:
self._viewer = rendering.SimpleImageViewer()
self._viewer.imshow(img.transpose(1, 2, 0))
def _get_game_variables(self, state_variables):
info = {}
if state_variables is not None:
info['KILLCOUNT'] = state_variables[0]
info['ITEMCOUNT'] = state_variables[1]
info['SECRETCOUNT'] = state_variables[2]
info['FRAGCOUNT'] = state_variables[3]
info['HEALTH'] = state_variables[4]
info['ARMOR'] = state_variables[5]
info['DEAD'] = state_variables[6]
info['ON_GROUND'] = state_variables[7]
info['ATTACK_READY'] = state_variables[8]
info['ALTATTACK_READY'] = state_variables[9]
info['SELECTED_WEAPON'] = state_variables[10]
info['SELECTED_WEAPON_AMMO'] = state_variables[11]
info['AMMO1'] = state_variables[12]
info['AMMO2'] = state_variables[13]
info['AMMO3'] = state_variables[14]
info['AMMO4'] = state_variables[15]
info['AMMO5'] = state_variables[16]
info['AMMO6'] = state_variables[17]
info['AMMO7'] = state_variables[18]
info['AMMO8'] = state_variables[19]
info['AMMO9'] = state_variables[20]
info['AMMO0'] = state_variables[21]
return info
class DoomScenario:
"""
DoomScenario class runs instances of Vizdoom according to scenario
configuration (.cfg) files.
Scenario Configuration files for this project are located in
the /src/configs/ folder.
"""
def __init__(self, config_filename):
'''
Method initiates Vizdoom with desired configuration file.
'''
self.config_filename = config_filename
self.game = DoomGame()
self.game.load_config("configs/" + config_filename)
self.game.set_window_visible(False)
self.game.init()
self.res = (self.game.get_screen_height(),
self.game.get_screen_width())
self.actions = [
list(a)
for a in it.product([0, 1],
repeat=self.game.get_available_buttons_size())
]
self.pbar = None
self.game.new_episode()
def play(self, action, tics):
'''
Method advances state with desired action for a number of tics.
'''
self.game.set_action(action)
self.game.advance_action(tics, True)
if self.pbar: self.pbar.update(int(tics))
def get_processed_state(self, depth_radius, depth_contrast):
'''
Method processes the Vizdoom RGB and depth buffer into
a composite one channel image that can be used by the Models.
depth_radius defines how far the depth buffer sees with 1.0 being
as far as ViZDoom allows.
depth_contrast defines how much of the depth buffer is in the final
processed image as compared to the greyscaled RGB buffer.
**processed = (1-depth_contrast)* grey_buffer + depth_contrast*depth_buffer
'''
state = self.game.get_state()
if not self.game.is_episode_finished():
img = state.screen_buffer # screen pixels
# print(img)
screen_buffer = np.array(img).astype('float32') / 255
# print(screen_buffer.shape) # (3, 120, 160)
try:
# Grey Scaling
grey_buffer = np.dot(np.transpose(screen_buffer, (1, 2, 0)),
[0.21, 0.72, 0.07])
# print(grey_buffer.shape) # (120, 160)
# Depth Radius
depth_buffer = np.array(state.depth_buffer).astype('float32') / 255
depth_buffer[(depth_buffer >
depth_radius)] = depth_radius #Effects depth radius
depth_buffer_filtered = (depth_buffer - np.amin(depth_buffer)) / (
np.amax(depth_buffer) - np.amin(depth_buffer))
# Depth Contrast
processed_buffer = (
(1 - depth_contrast) * grey_buffer) + (depth_contrast *
(1 - depth_buffer))
processed_buffer = (processed_buffer - np.amin(processed_buffer)
) / (np.amax(processed_buffer) -
np.amin(processed_buffer))
processed_buffer = np.round(processed_buffer, 6)
processed_buffer = processed_buffer.reshape(self.res[-2:])
except:
processed_buffer = np.zeros(self.res[-2:])
return processed_buffer # balance the depth & RGB data
def run(self, agent, save_replay='', verbose=False, return_data=False):
'''
Method runs a instance of DoomScenario.
'''
if return_data:
data_S = []
data_a = []
if verbose:
print("\nRunning Simulation:", self.config_filename)
self.pbar = tqdm(total=self.game.get_episode_timeout())
# Initiate New Instance
self.game.close()
self.game.set_window_visible(False)
self.game.add_game_args("+vid_forcesurface 1 ")
self.game.init()
if save_replay != '':
self.game.new_episode("../data/replay_data/" + save_replay)
else:
self.game.new_episode()
# Run Simulation
while not self.game.is_episode_finished():
S = agent.get_state_data(self)
q = agent.model.online_network.predict(S)
if np.random.random() < 0.1:
q = np.random.choice(len(q[0]), 1, p=softmax(q[0], 1))[0]
else:
q = int(np.argmax(q[0]))
a = agent.model.predict(self, q)
if return_data:
delta = np.zeros((len(self.actions)))
a_ = np.cast['int'](a)
delta[a_] = 1
data_S.append(S.reshape(S.shape[1], S.shape[2], S.shape[3]))
data_a.append(delta)
if not self.game.is_episode_finished():
self.play(a, agent.frame_skips + 1)
if agent.model.__class__.__name__ == 'HDQNModel' and not self.game.is_episode_finished(
):
if q >= len(agent.model.actions):
for i in range(agent.model.skill_frame_skip):
if not self.game.is_episode_finished():
a = agent.model.predict(self, q)
self.play(a, agent.frame_skips + 1)
else:
break
# Reset Agent and Return Score
agent.frames = None
if agent.model.__class__.__name__ == 'HDQNModel':
agent.model.sub_model_frames = None
score = self.game.get_total_reward()
if verbose:
self.pbar.close()
print("Total Score:", score)
if return_data:
data_S = np.array(data_S)
data_a = np.array(data_a)
return [data_S, data_a]
return score
def replay(self, filename, verbose=False, doom_like=False):
'''
Method runs a replay of the simulations at 800 x 600 resolution.
'''
print("\nRunning Replay:", filename)
# Initiate Replay
self.game.close()
self.game.set_screen_resolution(ScreenResolution.RES_800X600)
self.game.set_window_visible(True)
self.game.add_game_args("+vid_forcesurface 1")
if doom_like:
self.game.set_render_hud(True)
self.game.set_render_minimal_hud(False)
self.game.set_render_crosshair(False)
self.game.set_render_weapon(True)
self.game.set_render_particles(True)
self.game.init()
self.game.replay_episode("../data/replay_data/" + filename)
# Run Replay
while not self.game.is_episode_finished():
if verbose: print("Reward:", self.game.get_last_reward())
self.game.advance_action()
# Print Score
score = self.game.get_total_reward()
print("Total Score:", score)
self.game.close()
def apprentice_run(self, test=False):
'''
Method runs an apprentice data gathering.
'''
# Initiate New Instance
self.game.close()
self.game.set_mode(Mode.SPECTATOR)
self.game.set_screen_resolution(ScreenResolution.RES_800X600)
self.game.set_window_visible(True)
self.game.set_ticrate(30)
self.game.init()
self.game.new_episode()
# Run Simulation
while not self.game.is_episode_finished():
self.game.advance_action()
self.game.close()
class VizDoomEnv(gym.Env):
'''
Wrapper for vizdoom to use as an OpenAI gym environment.
'''
metadata = {'render.modes': ['human', 'rgb_array']}
def __init__(self, params):
super(VizDoomEnv, self).__init__()
self.params = params
self.game = DoomGame()
self.game.load_config(params.scenarioPath)
self._viewer = None
self.frameskip = params.frameskip
self.inputShape = params.inputShape
self.sequenceLength = params.sequenceLength
self.seqInputShape = (self.inputShape[0] * self.sequenceLength,
self.inputShape[1], self.inputShape[2])
self.gameVariables = params.gameVariables
self.numGameVariables = len(self.gameVariables)
self.action_space = spaces.MultiDiscrete(
[2] * self.game.get_available_buttons_size())
self.action_space.dtype = 'uint8'
output_shape = (self.game.get_screen_channels(),
self.game.get_screen_height(),
self.game.get_screen_width())
self.observation_space = spaces.Box(low=0,
high=255,
shape=output_shape,
dtype='uint8')
self.game.init()
# Maintain a buffer of last seq len frames.
self.frameBuffer = [np.zeros(self.inputShape)] * self.sequenceLength
def close(self):
self.game.close()
if self._viewer is not None:
self._viewer.close()
self._viewer = None
def seed(self, seed=None):
self.game.set_seed(seed)
def step(self, action):
reward = self.game.make_action(list(action), self.frameskip)
state = self.game.get_state()
done = self.game.is_episode_finished()
if state is not None:
observation = state.screen_buffer
info = state.game_variables # Return the chosen game variables in info
else:
observation = np.zeros(shape=self.observation_space.shape,
dtype=np.uint8)
info = None
processedObservation = self._preProcessImage(observation)
del self.frameBuffer[0]
self.frameBuffer.append(processedObservation)
return self.frameBuffer, reward, done, info
# Preprocess image for use in network
def _preProcessImage(self, image):
if image.shape != self.inputShape:
image = cv2.resize(image.transpose(1, 2, 0),
(self.inputShape[2], self.inputShape[1]),
interpolation=cv2.INTER_AREA).transpose(
2, 0, 1)
return image
def reset(self):
self.game.new_episode()
state = self._preProcessImage(self.game.get_state().screen_buffer)
self.frameBuffer = [state] * self.sequenceLength
return self.frameBuffer
def render(self, mode='human', close=False):
if close:
if self._viewer is not None:
self._viewer.close()
self._viewer = None
return
img = None
state = self.game.get_state()
if state is not None:
img = state.screen_buffer
if img is None:
# at the end of the episode
img = np.zeros(shape=self.observation_space.shape, dtype=np.uint8)
if mode == 'rgb_array':
return img
elif mode is 'human':
if self._viewer is None:
self._viewer = rendering.SimpleImageViewer()
self._viewer.imshow(img.transpose(1, 2, 0))
class VizdoomEnv(gym.Env):
def __init__(self, level):
# init game
self.game = DoomGame()
self.game.set_screen_resolution(ScreenResolution.RES_640X480)
scenarios_dir = os.path.join(os.path.dirname(__file__), 'scenarios')
self.game.load_config(os.path.join(scenarios_dir, CONFIGS[level][0]))
self.game.set_window_visible(False)
self.game.init()
self.state = None
self.action_space = spaces.Discrete(CONFIGS[level][1])
self.observation_space = spaces.Box(
0,
255, (self.game.get_screen_height(), self.game.get_screen_width(),
self.game.get_screen_channels()),
dtype=np.uint8)
self.viewer = None
def step(self, action):
# convert action to vizdoom action space (one hot)
act = np.zeros(self.action_space.n)
act[action] = 1
act = np.uint8(act)
act = act.tolist()
reward = self.game.make_action(act)
state = self.game.get_state()
done = self.game.is_episode_finished()
info = {}
if not done:
observation = np.transpose(state.screen_buffer, (1, 2, 0))
else:
observation = np.uint8(np.zeros(self.observation_space.shape))
info = {"episode": {"r": self.game.get_total_reward()}}
return observation, reward, done, info
def seed(self, seed):
self.game.set_seed(seed)
def close(self):
self.game.close()
def reset(self):
self.game.new_episode()
self.state = self.game.get_state()
img = self.state.screen_buffer
return np.transpose(img, (1, 2, 0))
def render(self, mode='human'):
try:
img = self.game.get_state().screen_buffer
img = np.transpose(img, [1, 2, 0])
if self.viewer is None:
self.viewer = rendering.SimpleImageViewer()
self.viewer.imshow(img)
except AttributeError:
pass
@staticmethod
def get_keys_to_action():
# you can press only one key at a time!
keys = {
(): 2,
(ord('a'), ): 0,
(ord('d'), ): 1,
(ord('w'), ): 3,
(ord('s'), ): 4,
(ord('q'), ): 5,
(ord('e'), ): 6
}
return keys
