def create_environment():
game = DoomGame()
game.load_config("basic.cfg")
game.set_doom_scenario_path("basic.wad")
game.init()
left = [1, 0, 0]
right = [0, 1, 0]
shoot = [0, 0, 1]
possible_actions = [left, right, shoot]
return game, possible_actions
def create_environment(episode_render=True):
game = DoomGame()
game.load_config('basic.cfg')
game.set_doom_scenario_path('basic.wad')
game.set_window_visible(episode_render)
game.init()
left = [1, 0, 0]
right = [0, 1, 0]
shoot = [0, 0, 1]
possible_actions = [left, right, shoot]
return game, possible_actions
def create_environment():
game = DoomGame()
game.load_config('defend_the_center.cfg')
game.set_doom_scenario_path('defend_the_center.wad')
game.init()
possible_actions = np.identity(3, dtype=int).tolist()
return game, possible_actions
def _vizdoom_setup(self, wad): game = DoomGame() game.load_config(DEFAULT_CONFIG) game.set_doom_scenario_path(wad) game.init() self.game = game
def init(buttons):
print("init")
game = DoomGame()
game.set_vizdoom_path("../../ViZDoom/bin/vizdoom")
game.set_doom_game_path("../../ViZDoom/scenarios/freedoom2.wad")
game.set_doom_scenario_path("../../ViZDoom/scenarios/basic.wad")
game.set_doom_map("map01")
game.set_screen_resolution(ScreenResolution.RES_320X240)
game.set_screen_format(ScreenFormat.RGB24)
game.set_depth_buffer_enabled(True)
game.set_labels_buffer_enabled(True)
game.set_automap_buffer_enabled(True)
# Sets other rendering options
game.set_render_hud(False)
game.set_render_minimal_hud(False)
game.set_render_crosshair(False)
game.set_render_weapon(True)
game.set_render_decals(False)
game.set_render_particles(False)
game.set_render_effects_sprites(False)
# Adds buttons that will be allowed.
for button in buttons:
game.add_available_button(button)
# Adds game variables that will be included in state.
game.add_available_game_variable(GameVariable.AMMO2)
game.add_available_game_variable(GameVariable.SELECTED_WEAPON)
# Causes episodes to finish after 200 tics (actions)
game.set_episode_timeout(300)
# Makes episodes start after 10 tics (~after raising the weapon)
game.set_episode_start_time(10)
# Makes the window appear (turned on by default)
game.set_window_visible(True)
# Turns on the sound. (turned off by default)
game.set_sound_enabled(True)
# Sets the livin reward (for each move) to -1
game.set_living_reward(-1)
# Sets ViZDoom mode (PLAYER, ASYNC_PLAYER, SPECTATOR, ASYNC_SPECTATOR, PLAYER mode is default)
game.set_mode(Mode.PLAYER)
# Initialize the game. Further configuration won't take any effect from now on.
# game.set_console_enabled(True)
game.init()
return game
def __init__(self, vizdoom_dir=os.path.expanduser('~/ViZDoom'),
window_visible=True, scenario='basic', skipcount=10,
resolution_width=640, sleep=0.0, seed=None):
self.skipcount = skipcount
self.sleep = sleep
sys.path.append(os.path.join(vizdoom_dir, "examples/python"))
from vizdoom import DoomGame
from vizdoom import ScreenFormat
from vizdoom import ScreenResolution
game = DoomGame()
if seed is not None:
assert seed >= 0 and seed < 2 ** 16, \
"ViZDoom's random seed must be represented by unsigned int"
else:
# Use numpy's random state
seed = np.random.randint(0, 2 ** 16)
game.set_seed(seed)
# Load a config file
game.load_config(os.path.join(
vizdoom_dir, "examples", 'config', scenario + '.cfg'))
# Replace default relative paths with actual paths
game.set_vizdoom_path(os.path.join(vizdoom_dir, "bin/vizdoom"))
game.set_doom_game_path(
os.path.join(vizdoom_dir, 'scenarios/freedoom2.wad'))
game.set_doom_scenario_path(
os.path.join(vizdoom_dir, 'scenarios', scenario + '.wad'))
# Set screen settings
resolutions = {640: ScreenResolution.RES_640X480,
320: ScreenResolution.RES_320X240,
160: ScreenResolution.RES_160X120}
game.set_screen_resolution(resolutions[resolution_width])
game.set_screen_format(ScreenFormat.RGB24)
game.set_window_visible(window_visible)
game.set_sound_enabled(window_visible)
game.init()
self.game = game
# Use one-hot actions
self.n_actions = game.get_available_buttons_size()
self.actions = []
for i in range(self.n_actions):
self.actions.append([i == j for j in range(self.n_actions)])
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()
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 new_episode(game: DoomGame, spawn_point_counter: Dict[int, int], n_spawn_points: int) -> None:
"""
Workaround for improper random number generation with ACS.
In certain scenarios the agent is spawned at a random spawn point.
However, instead of this distribution being uniform, one single id
is heavily preferred. In order to not have the agent encounter too
much of the same starting points, this method creates new episodes
until one is found with a different id than the most prominent one.
:param game: The instance of VizDoom
:param spawn_point_counter: The dict holding the counts of the previous spawn points
:param n_spawn_points: Number of spawn points in a given scenario
"""
while True:
game.new_episode()
spawn_point = game.get_game_variable(GameVariable.USER1)
spawn_point %= 21
if spawn_point == 0 or spawn_point is math.isnan(spawn_point):
return # Spawn point undefined
if spawn_point in spawn_point_counter:
spawn_point_counter[spawn_point] += 1
else:
spawn_point_counter[spawn_point] = 0
if spawn_point != max(spawn_point_counter, key = spawn_point_counter.get) and len(spawn_point_counter) >= n_spawn_points:
return
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 __init__(self, config='vizdoom_env/asset/default.cfg', verbose=False,
perception_type='more_simple'):
self.verbose = verbose
self.game = DoomGame()
self.game.load_config(config)
if self.verbose:
self.game.set_window_visible(True)
self.game.set_screen_resolution(ScreenResolution.RES_1280X960)
self.game_variables = self.game.get_available_game_variables()
self.buttons = self.game.get_available_buttons()
self.action_strings = [b.__str__().replace('Button.', '')
for b in self.buttons]
self.game_variable_strings = [v.__str__().replace('GameVariable.', '')
for v in self.game_variables]
self.perception_type = perception_type
if perception_type == 'clear':
self.distance_dict = CLEAR_DISTANCE_DICT
self.horizontal_dict = CLEAR_HORIZONTAL_DICT
elif perception_type == 'simple':
pass
elif perception_type == 'more_simple':
pass
else:
self.distance_dict = DISTANCE_DICT
self.horizontal_dict = HORIZONTAL_DICT
def __init__(self, level):
self.previous_level = -1
self.level = level
self.game = DoomGame()
self.loader = Loader()
self.doom_dir = os.path.dirname(os.path.abspath(__file__))
self._mode = 'algo' # 'algo' or 'human'
self.no_render = False # To disable double rendering in human mode
self.viewer = None
self.is_initialized = False # Indicates that reset() has been called
self.curr_seed = 0
self.lock = (DoomLock()).get_lock()
# self.action_space = spaces.Discrete(43) # used to be in the old code
self.action_space = spaces.MultiBinary(NUM_ACTIONS)
self.allowed_actions = list(range(NUM_ACTIONS))
self.screen_height = 120
self.screen_width = 160
self.screen_resolution = ScreenResolution.RES_160X120
self.observation_space = spaces.Box(low=0,
high=255,
shape=(self.screen_height,
self.screen_width, 3),
dtype=np.uint8)
self.seed()
self._configure()
def __init__(self, scenario, path_to_config="doom/config"):
self.game = DoomGame()
self.game.load_config(path_to_config + "/" + scenario + ".cfg")
self.game.set_doom_scenario_path(path_to_config + "/" + scenario +
".wad")
self.game.set_window_visible(False)
self.game.init()
self.num_actions = len(self.game.get_available_buttons())
def __init__(self, config, visible, skiprate):
self._game = DoomGame()
self._game.load_config(config)
self._game.set_window_visible(visible)
self._game.set_mode(Mode.PLAYER)
self._game.init()
n_actions = self._game.get_available_buttons_size()
self._actions = [list(a) for a in it.product([0, 1], repeat=n_actions)]
self._skiprate = skiprate
def __init__(self, config='my_way_home.cfg', repeat_action=1, render=False): self._game = DoomGame() self._game.load_config(config) self._game.set_mode(Mode.PLAYER) self._game.set_screen_format(ScreenFormat.GRAY8) self._game.set_screen_resolution(ScreenResolution.RES_640X480) self._game.set_window_visible(render) self._game.init() self._actions = self._get_actions() self._repeat_action = repeat_action self._is_rendered = False
def __init__(self, cfg_name, repeat=1):
super().__init__()
self.game = DoomGame()
self.game.load_config(f'./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_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()
def __init__(self, config_file):
"""
Initialize ViZDoom environment.
Args:
config_file: .cfg file path, which defines how a world works and look like (maps)
"""
self.game = DoomGame()
# load configurations from file
self.game.load_config(config_file)
self.game.init()
self.state_shape = self.featurize(self.game.get_state()).shape
self.num_actions = len(self.game.get_available_buttons())
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 __init__(self,
level,
visualize=False,
include_variables=False,
factored_action=False,
frame_skip=12,
seed=None):
super().__init__()
from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution
self.config_file = level
self.include_variables = include_variables
self.factored_action = factored_action
self.visualize = visualize
self.frame_skip = frame_skip
self.environment = DoomGame()
self.environment.load_config(self.config_file)
if self.visualize:
self.environment.set_window_visible(True)
self.environment.set_mode(Mode.ASYNC_PLAYER)
else:
self.environment.set_window_visible(False)
self.environment.set_mode(Mode.PLAYER)
# e.g. CRCGCB, RGB24, GRAY8
self.environment.set_screen_format(ScreenFormat.RGB24)
# e.g. RES_320X240, RES_640X480, RES_1920X1080
self.environment.set_screen_resolution(ScreenResolution.RES_640X480)
self.environment.set_depth_buffer_enabled(False)
self.environment.set_labels_buffer_enabled(False)
self.environment.set_automap_buffer_enabled(False)
if seed is not None:
self.environment.setSeed(seed)
self.environment.init()
self.state_shape = (480, 640, 3)
self.num_variables = self.environment.get_available_game_variables_size(
)
self.num_buttons = self.environment.get_available_buttons_size()
self.available_actions = [
tuple(a)
for a in itertools.product([0, 1], repeat=self.num_buttons)
]
def __init__(self, level):
self.previous_level = -1
self.level = level
self.game = DoomGame()
self.loader = Loader()
self.doom_dir = os.path.dirname(os.path.abspath(__file__))
self._mode = 'algo' # 'algo' or 'human'
self.no_render = False # To disable double rendering in human mode
self.viewer = None
self.is_initialized = False # Indicates that reset() has been called
self.curr_seed = 0
self.lock = (DoomLock()).get_lock()
self.action_space = spaces.MultiDiscrete([[0, 1]] * 38 + [[-10, 10]] * 2 + [[-100, 100]] * 3)
self.allowed_actions = list(range(NUM_ACTIONS))
self.screen_height = 480
self.screen_width = 640
self.screen_resolution = ScreenResolution.RES_640X480
self.observation_space = spaces.Box(low=0, high=255, shape=(self.screen_height, self.screen_width, 3))
self._seed()
self._configure()
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 __init__(self,
name: str,
root_dir: str,
task: str,
trained_task: str,
window_visible: bool,
n_tasks: int,
render_hud: bool,
name_addition: str,
sound_enabled = False,
variable_history_size = 5,
screen_resolution = ScreenResolution.RES_640X480
):
self.variable_history_size = variable_history_size
# Naming
self.name = name
self.root_dir = root_dir
self.name_addition = name_addition
# Tasks
self.n_tasks = n_tasks
self.task = task.lower()
self.trained_task = trained_task
# VizDoom
self.game = DoomGame()
self.game.load_config(self.config_path)
self.game.set_doom_scenario_path(self.scenario_path)
self.game.set_sound_enabled(sound_enabled)
self.game.set_window_visible(window_visible)
self.game.set_screen_resolution(screen_resolution)
self.game.set_render_hud(render_hud)
# Include the available tasks to the enum
for task in self.task_list:
extend_enum(Scenario.Task, task, auto())
self.model.load_weights(name)
# save the model which is under training
def save_model(self, name):
self.model.save_weights(name)
if __name__ == "__main__":
# Avoid Tensorflow eats up GPU memory
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
K.set_session(sess)
game = DoomGame()
game.load_config("../../scenarios/health_gathering.cfg")
game.set_sound_enabled(True)
game.set_screen_resolution(ScreenResolution.RES_640X480)
game.set_window_visible(False)
game.init()
game.new_episode()
game_state = game.get_state()
misc = game_state.game_variables # [Health]
prev_misc = misc
action_size = game.get_available_buttons_size() # [Turn Left, Turn Right, Move Forward]
measurement_size = 3 # [Health, Medkit, Poison]
timesteps = [1,2,4,8,16,32]
goal_size = measurement_size * len(timesteps)
def doom_game():
game = DoomGame()
#game.load_config("../scenarios/basic.cfg")
game.load_config("../scenarios/defend_the_center.cfg")
#game.set_doom_map("map01")
game.set_screen_resolution(ScreenResolution.RES_320X240)
#game.set_screen_resolution(ScreenResolution.RES_640X480)
game.set_render_hud(False)
game.set_render_crosshair(False)
game.set_render_weapon(True)
game.set_render_decals(False)
game.set_render_particles(False)
#game.add_available_button(Button.MOVE_LEFT)
#game.add_available_button(Button.MOVE_RIGHT)
game.add_available_button(Button.TURN_LEFT)
game.add_available_button(Button.TURN_RIGHT)
game.add_available_button(Button.ATTACK)
game.set_episode_timeout(2100)
game.set_episode_start_time(10)
game.set_window_visible(True) #False)
game.set_sound_enabled(False)
game.set_living_reward(0.2) # -1 for basic
game.set_mode(Mode.PLAYER)
game.init()
return game
self.critic.save_weights(name + "_critic.h5", overwrite=True)
def load_model(self, name):
self.actor.load_weights(name + "_actor.h5", overwrite=True)
self.critic.load_weights(name + "_critic.h5", overwrite=True)
if __name__ == "__main__":
# Avoid Tensorflow eats up GPU memory
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=config)
K.set_session(sess)
game = DoomGame()
game.load_config("../../scenarios/defend_the_center.cfg")
game.set_sound_enabled(True)
game.set_screen_resolution(ScreenResolution.RES_640X480)
game.set_window_visible(False)
game.init()
# Maximum number of episodes
max_episodes = 1000000
game.new_episode()
game_state = game.get_state()
misc = game_state.game_variables # [KILLCOUNT, AMMO, HEALTH]
prev_misc = misc
action_size = game.get_available_buttons_size()
from __future__ import print_function
from vizdoom import DoomGame
from vizdoom import Mode
from vizdoom import Button
from vizdoom import GameVariable
from vizdoom import ScreenFormat
from vizdoom import ScreenResolution
# Or just use from vizdoom import *
from random import choice
from time import sleep
from time import time
# Create DoomGame instance. It will run the game and communicate with you.
game = DoomGame()
# Now it's time for configuration!
# load_config could be used to load configuration instead of doing it here with code.
# If load_config is used in-code configuration will work. Note that the most recent changes will add to previous ones.
#game.load_config("../../examples/config/basic.cfg")
# Sets path to vizdoom engine executive which will be spawned as a separate process. Default is "./vizdoom".
game.set_vizdoom_path("../../bin/vizdoom")
# Sets path to doom2 iwad resource file which contains the actual doom game. Default is "./doom2.wad".
game.set_doom_game_path("../../scenarios/freedoom2.wad")
#game.set_doom_game_path("../../scenarios/doom2.wad") # Not provided with environment due to licences.
# Sets path to additional resources iwad file which is basically your scenario iwad.
# If not specified default doom2 maps will be used and it's pretty much useles... unless you want to play doom.
#####################################################################
from __future__ import print_function
from vizdoom import DoomGame
from vizdoom import Mode
from vizdoom import Button
from vizdoom import GameVariable
from vizdoom import ScreenFormat
from vizdoom import ScreenResolution
# Or just use from vizdoom import *
from random import choice
from time import sleep
from time import time
# Create DoomGame instance. It will run the game and communicate with you.
game = DoomGame()
# Now it's time for configuration!
# load_config could be used to load configuration instead of doing it here with code.
# If load_config is used in-code configuration will work. Note that the most recent changes will add to previous ones.
#game.load_config("../../examples/config/basic.cfg")
# Sets path to vizdoom engine executive which will be spawned as a separate process. Default is "./vizdoom".
game.set_vizdoom_path("../../bin/vizdoom")
# Sets path to doom2 iwad resource file which contains the actual doom game. Default is "./doom2.wad".
game.set_doom_game_path("../../scenarios/freedoom2.wad")
#game.set_doom_game_path("../../scenarios/doom2.wad") # Not provided with environment due to licences.
# Sets path to additional resources iwad file which is basically your scenario iwad.
# If not specified default doom2 maps will be used and it's pretty much useles... unless you want to play doom.
class VizDoom(gym.Env):
"""
Wraps a VizDoom environment
"""
def __init__(self,
cfg_path,
number_maps,
scaled_resolution=(42, 42),
action_frame_repeat=4,
clip=(-1, 1),
seed=None,
data_augmentation=False):
"""
Gym environment for training reinforcement learning agents.
:param cfg_path: name of the mission (.cfg) to run
:param number_maps: number of maps which are contained within the cfg file
:param scaled_resolution: resolution (height, width) of the observation to be returned with each step
:param action_frame_repeat: how many game tics should an action be active
:param clip: how much the reward returned on each step should be clipped to
:param seed: seed for random, used to determine the other that the doom maps should be shown.
:param data_augmentation: bool to determine whether or not to use data augmentation
(adding randomly colored, randomly sized boxes to observation)
"""
self.cfg_path = str(cfg_path)
if not os.path.exists(self.cfg_path):
raise ValueError("Cfg file not found", cfg_path)
if not self.cfg_path.endswith('.cfg'):
raise ValueError("cfg_path must end with .cfg")
self.number_maps = number_maps
self.scaled_resolution = scaled_resolution
self.action_frame_repeat = action_frame_repeat
self.clip = clip
self.data_augmentation = data_augmentation
if seed:
random.seed(seed)
super(VizDoom, self).__init__()
self._logger = logging.getLogger(__name__)
self._logger.info("Creating environment: VizDoom (%s)", self.cfg_path)
# Create an instace on VizDoom game, initalise it from a scenario config file
self.env = DoomGame()
self.env.load_config(self.cfg_path)
self.env.init()
# Perform config validation:
# Only RGB format with a seperate channel per colour is supported
# assert self.env.get_screen_format() == ScreenFormat.RGB24
# Only discreete actions are supported (no delta actions)
available_actions = self.env.get_available_buttons()
not_supported_actions = [
Button.LOOK_UP_DOWN_DELTA, Button.TURN_LEFT_RIGHT_DELTA,
Button.MOVE_LEFT_RIGHT_DELTA, Button.MOVE_UP_DOWN_DELTA,
Button.MOVE_FORWARD_BACKWARD_DELTA
]
assert len((set(available_actions) -
set(not_supported_actions))) == len(available_actions)
# Allow only one button to be pressed at a given step
self.action_space = gym.spaces.Discrete(
self.env.get_available_buttons_size())
rows = scaled_resolution[1]
columns = scaled_resolution[0]
self.observation_space = gym.spaces.Box(0.0,
255.0,
shape=(columns, rows, 3),
dtype=np.float32)
self._rgb_array = None
self.reset()
def _process_image(self, shape=None):
"""
Convert the vizdoom environment observation numpy are into the desired resolution and shape
:param shape: desired shape in the format (rows, columns)
:return: resized and rescaled image in the format (rows, columns, channels)
"""
if shape is None:
rows, columns, _ = self.observation_space.shape
else:
rows, columns = shape
# PIL resize has indexing opposite to numpy array
img = VizDoom._resize(self._rgb_array.transpose(1, 2, 0),
(columns, rows))
return img
@staticmethod
def _augment_data(img):
"""
Augment input image with N randomly colored boxes of dimension x by y
where N is randomly sampled between 0 and 6
and x and y are randomly sampled from between 0.1 and 0.35
:param img: input image to be augmented - format (rows, columns, channels)
:return img: augmented image - format (rows, columns, channels)
"""
dimx = img.shape[0]
dimy = img.shape[1]
max_rand_dim = .25
min_rand_dim = .1
num_blotches = np.random.randint(0, 6)
for _ in range(num_blotches):
# locations in [0,1]
rand = np.random.rand
rx = rand()
ry = rand()
rdx = rand() * max_rand_dim + min_rand_dim
rdy = rand() * max_rand_dim + min_rand_dim
rx, rdx = [round(r * dimx) for r in (rx, rdx)]
ry, rdy = [round(r * dimy) for r in (ry, rdy)]
for c in range(3):
img[rx:rx + rdx, ry:ry + rdy, c] = np.random.randint(0, 255)
return img
@staticmethod
def _resize(img, shape):
"""Resize the specified image.
:param img: image to resize
:param shape: desired shape in the format (rows, columns)
:return: resized image
"""
if not (OPENCV_AVAILABLE or PILLOW_AVAILABLE):
raise ValueError('No image library backend found.'
' Install either '
'OpenCV or Pillow to support image processing.')
if OPENCV_AVAILABLE:
return cv2.resize(img, shape, interpolation=cv2.INTER_AREA)
if PILLOW_AVAILABLE:
return np.array(PIL.Image.fromarray(img).resize(shape))
raise NotImplementedError
def reset(self):
"""
Resets environment to start a new mission.
If there is more than one maze it will randomly select a new maze.
:return: initial observation of the environment as an rgb array in the format (rows, columns, channels)
"""
if self.number_maps is not 0:
self.doom_map = random.choice(
["map" + str(i).zfill(2) for i in range(self.number_maps)])
self.env.set_doom_map(self.doom_map)
self.env.new_episode()
self._rgb_array = self.env.get_state().screen_buffer
observation = self._process_image()
return observation
def step(self, action):
"""Perform the specified action for the self.action_frame_repeat ticks within the environment.
:param action: the index of the action to perform. The actions are specified when the cfg is created. The
defaults are "MOVE_FORWARD TURN_LEFT TURN_RIGHT"
:return: tuple following the gym interface, containing:
- observation as a numpy array of shape (rows, height, channels)
- scalar clipped reward
- boolean which is true when the environment is done
- {}
"""
one_hot_action = np.zeros(self.action_space.n, dtype=int)
one_hot_action[action] = 1
reward = self.env.make_action(list(one_hot_action),
self.action_frame_repeat)
done = self.env.is_episode_finished()
# state is available only if the episode is still running
if not done:
self._rgb_array = self.env.get_state().screen_buffer
observation = self._process_image()
if self.data_augmentation:
observation = VizDoom._augment_data(observation)
if self.clip:
reward = np.clip(reward, self.clip[0], self.clip[1])
return observation, reward, done, {}
def step_record(self, action, record_path, record_shape=(120, 140)):
"""Perform the specified action for the self.action_frame_repeat ticks within the environment.
:param action: the index of the action to perform. The actions are specified when the cfg is created. The
defaults are "MOVE_FORWARD TURN_LEFT TURN_RIGHT"
:param record_path: the path to save the image of the environment to
:param record_shape: the shape of the image to save
:return: tuple following the gym interface, containing:
- observation as a numpy array of shape (rows, height, channels)
- scalar clipped reward
- boolean which is true when the environment is done
- {}
"""
one_hot_action = np.zeros(self.action_space.n, dtype=int)
one_hot_action[action] = 1
reward = 0
for _ in range(self.action_frame_repeat // 2):
reward += self.env.make_action(list(one_hot_action), 2)
env_state = self.env.get_state()
if env_state:
self._rgb_array = self.env.get_state().screen_buffer
imageio.imwrite(
os.path.join(record_path,
str(datetime.datetime.now()) + ".png"),
self._process_image(record_shape))
done = self.env.is_episode_finished()
# state is available only if the episode is still running
if not done:
self._rgb_array = self.env.get_state().screen_buffer
observation = self._process_image()
if self.clip:
reward = np.clip(reward, self.clip[0], self.clip[1])
return observation, reward, done, {}
def close(self):
"""Close environment"""
self.env.close()
def render(self, mode='rgb_array'):
"""Render frame"""
if mode == 'rgb_array':
return self._rgb_array
raise NotImplementedError
def create_env(self):
"""
Returns a function to create an environment with the generated mazes.
Used for vectorising the environment. For example as used by Stable Baselines
:return: a function to create an environment with the generated mazes
"""
return lambda: VizDoom(self.cfg_path,
number_maps=self.number_maps,
scaled_resolution=self.scaled_resolution,
action_frame_repeat=self.action_frame_repeat)
def __init__(self,
cfg_path,
number_maps,
scaled_resolution=(42, 42),
action_frame_repeat=4,
clip=(-1, 1),
seed=None,
data_augmentation=False):
"""
Gym environment for training reinforcement learning agents.
:param cfg_path: name of the mission (.cfg) to run
:param number_maps: number of maps which are contained within the cfg file
:param scaled_resolution: resolution (height, width) of the observation to be returned with each step
:param action_frame_repeat: how many game tics should an action be active
:param clip: how much the reward returned on each step should be clipped to
:param seed: seed for random, used to determine the other that the doom maps should be shown.
:param data_augmentation: bool to determine whether or not to use data augmentation
(adding randomly colored, randomly sized boxes to observation)
"""
self.cfg_path = str(cfg_path)
if not os.path.exists(self.cfg_path):
raise ValueError("Cfg file not found", cfg_path)
if not self.cfg_path.endswith('.cfg'):
raise ValueError("cfg_path must end with .cfg")
self.number_maps = number_maps
self.scaled_resolution = scaled_resolution
self.action_frame_repeat = action_frame_repeat
self.clip = clip
self.data_augmentation = data_augmentation
if seed:
random.seed(seed)
super(VizDoom, self).__init__()
self._logger = logging.getLogger(__name__)
self._logger.info("Creating environment: VizDoom (%s)", self.cfg_path)
# Create an instace on VizDoom game, initalise it from a scenario config file
self.env = DoomGame()
self.env.load_config(self.cfg_path)
self.env.init()
# Perform config validation:
# Only RGB format with a seperate channel per colour is supported
# assert self.env.get_screen_format() == ScreenFormat.RGB24
# Only discreete actions are supported (no delta actions)
available_actions = self.env.get_available_buttons()
not_supported_actions = [
Button.LOOK_UP_DOWN_DELTA, Button.TURN_LEFT_RIGHT_DELTA,
Button.MOVE_LEFT_RIGHT_DELTA, Button.MOVE_UP_DOWN_DELTA,
Button.MOVE_FORWARD_BACKWARD_DELTA
]
assert len((set(available_actions) -
set(not_supported_actions))) == len(available_actions)
# Allow only one button to be pressed at a given step
self.action_space = gym.spaces.Discrete(
self.env.get_available_buttons_size())
rows = scaled_resolution[1]
columns = scaled_resolution[0]
self.observation_space = gym.spaces.Box(0.0,
255.0,
shape=(columns, rows, 3),
dtype=np.float32)
self._rgb_array = None
self.reset()
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()
def play(self):
# Create DoomGame instance. It will run the game and communicate with you.
print ("Initializing doom...")
game = DoomGame()
game.load_config("./examples/config/deepdoomplayer.cfg")
game.init()
print ("Doom initialized.")
episodes = 1
training_steps_per_epoch = 100
sleep_time = 0.100
train_episodes_finished = 0
train_rewards = []
for epoch in range(episodes):
train_loss = []
game.new_episode()
while(train_episodes_finished < 20 ):
sleep(sleep_time)
if game.is_episode_finished():
r = game.get_total_reward()
train_rewards.append(r)
game.new_episode()
train_episodes_finished += 1
self._last_state = None
self.last_action[1] = 1
# first frame must be handled differently
if self.last_state is None:
# the _last_state will contain the image data from the last self.state_frames frames
self.last_state = np.stack(tuple(self.convert_image(game.get_state().image_buffer) for _ in range(self.state_frames)), axis=2)
continue
reward = game.make_action(DeepDoomPlayer.define_keys_to_action_pressed(self.last_action), 7)
reward *= 0.01
imagebuffer = game.get_state().image_buffer
if imagebuffer is None:
terminal = True
screen_resized_binary = np.zeros((40,40))
imagebufferlast = imagebuffer
if imagebuffer is not None:
terminal = False
screen_resized_binary = self.convert_image(imagebuffer)
# add dimension
screen_resized_binary = np.expand_dims(screen_resized_binary, axis=2)
current_state = np.append(self.last_state[:, :, 1:], screen_resized_binary, axis=2)
self.last_state = current_state
self.last_action = self.choose_next_action_only_on_q()
print (train_episodes_finished, "training episodes played.")
print ("Training results:")
train_rewards = np.array(train_rewards)
print ("mean:", train_rewards.mean(),
"std:", train_rewards.std(),
"max:", train_rewards.max(),
"min:", train_rewards.min())
# It will be done automatically anyway but sometimes you need to do it in the middle of the program...
game.close()
self._last_state = None
#####################################################################
from __future__ import print_function
from vizdoom import DoomGame
from vizdoom import Mode
from vizdoom import Button
from vizdoom import GameVariable
from vizdoom import ScreenFormat
from vizdoom import ScreenResolution
# Or just use from vizdoom import *
from random import choice
from time import sleep
from time import time
# Create DoomGame instance. It will run the game and communicate with you.
game = DoomGame()
# Now it's time for configuration!
# load_config could be used to load configuration instead of doing it here with code.
# If load_config is used in-code configuration will work. Note that the most recent changes will add to previous ones.
# game.load_config("../../examples/config/basic.cfg")
# Sets path to vizdoom engine executive which will be spawned as a separate process. Default is "./vizdoom".
game.set_vizdoom_path("../bin/vizdoom")
# Sets path to doom2 iwad resource file which contains the actual doom game. Default is "./doom2.wad".
game.set_doom_game_path("../scenarios/freedoom2.wad")
# game.set_doom_game_path("../../scenarios/doom2.wad") # Not provided with environment due to licences.
# Sets path to additional resources iwad file which is basically your scenario iwad.
# If not specified default doom2 maps will be used and it's pretty much useles... unless you want to play doom.
#####################################################################
from __future__ import print_function
from vizdoom import DoomGame
from vizdoom import Button
from vizdoom import GameVariable
from vizdoom import ScreenFormat
from vizdoom import ScreenResolution
# Or just use from vizdoom import *
from random import choice
from time import sleep
from time import time
# Create DoomGame instance. It will run the game and communicate with you.
game = DoomGame()
# Now it's time for configuration!
# load_config could be used to load configuration instead of doing it here with code.
# If load_config is used in-code configuration will work. Note that the most recent changes will add to previous ones.
#game.load_config("../../examples/config/basic.cfg")
# Sets path to vizdoom engine executive which will be spawned as a separate process. Default is just the same.
game.set_doom_engine_path("../../bin/vizdoom")
# Sets path to doom2 iwad resource file which contains the actual doom game. Default is "./doom2.wad".
game.set_doom_game_path("../../scenarios/freedoom2.wad")
#game.set_doom_game_path("../../scenarios/doom2.wad") # Not provided with environment due to licences.
# Sets path to additional resources iwad file which is basically your scenario iwad.
# If not specified default doom2 maps will be used and it's pretty much useles... unless you want to play doom.
# Configuration is loaded from "../../scenarios/<SCENARIO_NAME>.cfg" file.
# <episodes> number of episodes are played.
# Random combination of buttons is chosen for every action.
# Game variables from state and last reward are printed.
#
# To see the scenario description go to "../../scenarios/README.md"
#####################################################################
from __future__ import print_function
import itertools as it
from random import choice
from time import sleep
from vizdoom import DoomGame, ScreenResolution
game = DoomGame()
# Choose scenario config file you wish to watch.
# Don't load two configs cause the second will overrite the first one.
# Multiple config files are ok but combining these ones doesn't make much sense.
# game.load_config("../../scenarios/basic.cfg")
# game.load_config("../../scenarios/simpler_basic.cfg")
game.load_config("../../scenarios/rocket_basic.cfg")
# game.load_config("../../scenarios/deadly_corridor.cfg")
# game.load_config("../../scenarios/deathmatch.cfg")
# game.load_config("../../scenarios/defend_the_center.cfg")
# game.load_config("../../scenarios/defend_the_line.cfg")
# game.load_config("../../scenarios/health_gathering.cfg")
# game.load_config("../../scenarios/my_way_home.cfg")
# game.load_config("../../scenarios/predict_position.cfg")
#changes made
import lua
import numpy as np
torch = lua.require('torch')
lua.require('trepl')
dqn = lua.eval("dofile('dqn/NeuralQLearner.lua')")
tt = lua.eval("dofile('dqn/TransitionTable.lua')")
# Create DoomGame instance. It will run the game and communicate with you.
game = DoomGame()
screen_width = 320
screen_height = 240
color_palette = 24
# Now it's time for configuration!
# load_config could be used to load configuration instead of doing it here with code.
# If load_config is used in-code configuration will work. Note that the most recent changes will add to previous ones.
#game.load_config("../../examples/config/basic.cfg")
# Sets path to vizdoom engine executive which will be spawned as a separate process. Default is "./vizdoom".
game.set_vizdoom_path("../../bin/vizdoom")
# Sets path to doom2 iwad resource file which contains the actual doom game. Default is "./doom2.wad".
game.set_doom_game_path("../../scenarios/freedoom2.wad")
#game.set_doom_game_path("../../scenarios/doom2.wad") # Not provided with environment due to licences.
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
# This script presents how to run some scenarios.
# Configuration is loaded from "../../examples/config/<SCENARIO_NAME>.cfg" file.
# <episodes> number of episodes are played.
# Random combination of buttons is chosen for every action.
# Game variables from state and last reward are printed.
# To see the scenario description go to "../../scenarios/README.md"
#
#####################################################################
from __future__ import print_function
import itertools as it
from random import choice
from time import sleep
from vizdoom import DoomGame, ScreenResolution
game = DoomGame()
# Choose scenario config file you wish to watch.
# Don't load two configs cause the second will overrite the first one.
# Multiple config files are ok but combining these ones doesn't make much sense.
game.load_config("../../examples/config/basic.cfg")
# game.load_config("../../examples/config/deadly_corridor.cfg")
# game.load_config("../../examples/config/deathmatch.cfg")
# game.load_config("../../examples/config/defend_the_center.cfg")
# game.load_config("../../examples/config/defend_the_line.cfg")
# game.load_config("../../examples/config/health_gathering.cfg")
# game.load_config("../../examples/config/my_way_home.cfg")
# game.load_config("../../examples/config/predict_position.cfg")
# game.load_config("../../examples/config/take_cover.cfg")
def start(self):
"""
this will get passed hier
"""
# Create DoomGame instance. It will run the game and communicate with you.
print ("Initializing doom...")
game = DoomGame()
game.load_config("./examples/config/learningtensorflow.cfg")
game.init()
print ("Doom initialized.")
train_rewards = []
for epoch in range(DeepDoom.episodes):
print ("\nEpoch", epoch)
train_time = 0
train_episodes_finished = 0
train_loss = []
#start saving after 20 epoch
if epoch > 20:
if not os.path.exists(DeepDoom.checkpoint_path):
os.mkdir(DeepDoom.checkpoint_path)
self.saver.save(self.session, DeepDoom.checkpoint_path, global_step=epoch )
train_start = time()
game.new_episode()
for learning_step in tqdm(range(DeepDoom.training_steps_per_epoch)):
if game.is_episode_finished():
#print("game is finished")
r = game.get_total_reward()
train_rewards.append(r)
game.new_episode()
train_episodes_finished += 1
self.last_state = None
#sleep(sleep_time)
# first frame must be handled differently
if self.last_state is None:
#print ("ich bin hier")
# the last_state will contain the image data from the last self.state_frames frames
self.last_state = np.stack(tuple(self.convert_image(game.get_state().image_buffer) for _ in range(self.state_frames)), axis=2)
continue
reward = game.make_action(DeepDoom.define_keys_to_action_pressed(self.last_action), 7)
reward *= 0.01
#if screen_array is not None:
imagebuffer = game.get_state().image_buffer
if imagebuffer is None:
terminal = True
#print(reward)
screen_resized_binary = np.zeros((40,40))
imagebufferlast = imagebuffer
if imagebuffer is not None:
terminal = False
screen_resized_binary = self.convert_image(imagebuffer)
# add dimension
screen_resized_binary = np.expand_dims(screen_resized_binary, axis=2)
current_state = np.append(self.last_state[:, :, 1:], screen_resized_binary, axis=2)
self.observations.append((self.last_state, self.last_action, reward, current_state, terminal))
#zeugs.write("oberservations %s \n" %len(self.observations))
if len(self.observations) > self.memory_size:
self.observations.popleft()
#sleep(sleep_time)
# only train if done observing
if len(self.observations) > self.observation_steps:
#print("train")
self.train()
self.time += 1
self.last_state = current_state
self.last_action = self.choose_next_action()
if self.probability_of_random_action > self.final_random_action_prob \
and len(self.observations) > self.observation_steps:
self.probability_of_random_action -= \
(self.initial_random_action_prob - self.final_random_action_prob) / self.explore_steps
print (train_episodes_finished, "training episodes played.")
print ("Training results:")
train_rewards = np.array(train_rewards)
train_end = time()
train_time = train_end - train_start
mean_loss = np.mean(train_loss)
print ("mean:", train_rewards.mean(), "std:", train_rewards.std(), "max:", train_rewards.max(), "min:", train_rewards.min(), "epsilon:", self.probability_of_random_action)
print ("t:", str(round(train_time, 2)) + "s")
train_rewards = []
# It will be done automatically anyway but sometimes you need to do it in the middle of the program...
game.close()
self.last_state = None
