class DoomEnvironment:
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 reset(self):
self.game.new_episode()
game_state = self.game.get_state()
obs = game_state.screen_buffer
self.h, self.w = obs.shape[1:3]
self.current_obs = self.preprocess_obs(obs)
if self.game.get_available_game_variables_size() == 2:
self.ammo, self.health = game_state.game_variables
return self.get_obs()
def get_obs(self):
return self.current_obs[:, :, None]
def get_obs_rgb(self):
img = self.game.get_state().screen_buffer
img = np.rollaxis(img, 0, 3)
img = np.reshape(img, [self.h, self.w, 3])
return img.astype(np.uint8)
def preprocess_obs(self, obs):
img = np.rollaxis(obs, 0, 3)
img = np.reshape(img, [self.h, self.w, 3]).astype(np.float32)
img = img[:, :, 0] * 0.299 + img[:, :, 1] * 0.587 + img[:, :,
2] * 0.114
img = Image.fromarray(img)
img = img.resize((84, 84), Image.BILINEAR)
img = np.array(img)
return img.astype(np.uint8)
def action_to_doom(self, a):
action = [0 for i in range(self.num_actions)]
action[int(a)] = 1
return action
def step(self, a):
action = self.action_to_doom(a)
reward = self.game.make_action(action)
done = self.game.is_episode_finished()
if done:
new_obs = np.zeros_like(self.current_obs, dtype=np.uint8)
else:
game_state = self.game.get_state()
new_obs = game_state.screen_buffer
new_obs = self.preprocess_obs(new_obs)
self.current_obs = new_obs
return self.get_obs(), reward, done
def watch_random_play(self, max_ep_length=1000, frame_skip=4):
self.reset()
for i in range(max_ep_length):
a = np.random.randint(self.num_actions)
obs, reward, done = self.step(a)
if done: break
img = self.get_obs_rgb()
if i % frame_skip == 0:
plt.imshow(img)
display.clear_output(wait=True)
display.display(plt.gcf())
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)
class ViZDoom(Environment):
"""
ViZDoom environment (https://github.com/mwydmuch/ViZDoom).
"""
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 __str__(self):
return 'ViZDoom'
def states(self):
return dict(type='float', shape=self.state_shape)
def actions(self):
return dict(type='int', shape=(), num_values=self.num_actions)
def close(self):
self.game.close()
def reset(self):
self.game.new_episode()
return self.featurize(self.game.get_state())
def seed(self, seed):
self.game.setSeed(seed)
return seed
def featurize(self, state):
H = state.screen_buffer.shape[0]
W = state.screen_buffer.shape[1]
_vars = state.game_variables.reshape(-1).astype(np.float32)
_screen_buf = state.screen_buffer.reshape(-1).astype(np.float32)
if state.depth_buffer is None:
_depth_buf = np.zeros(H * W * 1, dtype=np.float32)
else:
_depth_buf = state.depth_buffer.reshape(-1).astype(np.float32)
if state.labels_buffer is None:
_labels_buf = np.zeros(H * W * 1, dtype=np.float32)
else:
_labels_buf = state.labels_buffer.reshape(-1).astype(np.float32)
if state.automap_buffer is None:
_automap_buf = np.zeros(H * W * 1, dtype=np.float32)
else:
_automap_buf = state.automap_buffer.reshape(-1).astype(np.float32)
return np.concatenate(
(_vars, _screen_buf, _depth_buf, _labels_buf, _automap_buf))
def execute(self, action):
one_hot_enc = [0] * self.num_actions
one_hot_enc[action] = 1
reward = self.game.make_action(one_hot_enc)
terminal = self.game.is_episode_finished()
states = self.featurize(self.game.get_state())
return states, terminal, reward
class VizDoomGym(gym.Env):
"""
Wraps a VizDoom environment
"""
def __init__(self):
raise NotImplementedError
def _init(self, mission_file: str, scaled_resolution: list):
"""
:param mission_file: name of the mission (.cfg) to run,
:param scaled_resolution: resolution (height, width) of the video frames
to run training on
"""
super(VizDoomGym, self).__init__()
self.mission_file = mission_file
self._logger = logging.getLogger(__name__)
self._logger.info("Creating environment: VizDoom (%s)",
self.mission_file)
self.deathmatch = True
# distance we need the agent to travel per time-step, otherwise we penalise
self.distance_threshold = 15
self.prev_properties = None
self.properties = None
self.cum_kills = np.array([0])
# Create an instace on VizDoom game, initalise it from a scenario config file
self.env = DoomGame()
self.env.load_config(self.mission_file)
self.env.set_window_visible(False)
self.env.set_screen_format(ScreenFormat.RGB24)
if self.deathmatch:
self.env.add_game_args("-deathmatch")
self.env.set_doom_skill(4)
self._action_frame_repeat = 4
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 discrete actions are supported (no delta actions)
self.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
]
# print(available_actions)
assert len((set(self.available_actions) - set(not_supported_actions))) \
== len(self.available_actions)
self.metadata['render_modes'] = ['rgb_array']
# Allow only one button to be pressed at a given step
self.action_space = gym.spaces.Discrete(
self.env.get_available_buttons_size() - 1)
self.rows = scaled_resolution[0]
self.columns = scaled_resolution[1]
self.observation_space = gym.spaces.Box(low=0.0,
high=1.0,
shape=(self.rows, self.columns,
3),
dtype=np.float32)
self._rgb_array = None
self.steps = 0
self.global_steps = 0
self.reset()
def _process_image(self, img):
# PIL resize has indexing opposite to numpy array
img = np.array(Image.fromarray(img).resize((self.columns, self.rows)))
img = img.astype(np.float32)
img = img / 255.0
return img
def update_game_variables(self):
"""
Check and update game variables.
"""
# read game variables
new_v = {
k: self.env.get_game_variable(v)
for k, v in game_variables.items()
}
assert all(v.is_integer() or k[-2:] in ['_x', '_y', '_z']
for k, v in new_v.items())
new_v = {
k: (int(v) if v.is_integer() else float(v))
for k, v in new_v.items()
}
health = new_v['health']
armor = new_v['armor']
# check game variables
assert 0 <= health <= 200 or health < 0 and self.env.is_player_dead()
assert 0 <= armor <= 200, (health, armor)
# update actor properties
self.prev_properties = self.properties
self.properties = new_v
def update_reward(self):
"""
Update reward.
"""
# we need to know the current and previous properties
assert self.prev_properties is not None and self.properties is not None
reward = 0
# kill
d = self.properties['score'] - self.prev_properties['score']
if d > 0:
self.cum_kills += d
reward += d * default_reward_values['KILL']
# death
if self.env.is_player_dead():
reward += default_reward_values['DEATH']
# suicide
if self.properties['frag_count'] < self.prev_properties['frag_count']:
reward += default_reward_values['SUICIDE']
# found / lost health
d = self.properties['health'] - self.prev_properties['health']
if d != 0:
if d > 0:
reward += default_reward_values['MEDIKIT']
else:
reward += default_reward_values['INJURED']
# found / lost armor
d = self.properties['armor'] - self.prev_properties['armor']
if d != 0:
if d > 0:
reward += default_reward_values['ARMOR']
# found / lost ammo
d = self.properties['sel_ammo'] - self.prev_properties['sel_ammo']
if d != 0:
if d > 0:
reward += default_reward_values['AMMO']
else:
reward += default_reward_values['USE_AMMO']
# distance
# turn_left = (Button.TURN_LEFT == self.available_actions[action])
# turn_right = (Button.TURN_RIGHT == self.available_actions[action])
# if not (turn_left or turn_right):
diff_x = self.properties['position_x'] - self.prev_properties[
'position_x']
diff_y = self.properties['position_y'] - self.prev_properties[
'position_y']
distance = np.sqrt(diff_x**2 + diff_y**2)
if distance > self.distance_threshold:
reward += default_reward_values['DISTANCE'] * distance
else:
reward += default_reward_values['STANDSTILL']
# living
reward += default_reward_values['LIVING']
return reward
# def increase_difficulty(self):
# self.curr_skill += 1
# self.env.close()
# self.env.set_doom_skill(self.curr_skill)
# self.env.init()
# print('changing skill to', self.curr_skill)
# def update_map(self):
# self.map_level += 1
# map_str = 'map0' + str(self.map_level)
# # go with initial wad file if there's still maps on it
# self.env.close()
# self.env.set_doom_map(map_str)
# self.env.init()
def sub_reset(self):
"""Reset environment"""
self.steps = 0
self.cum_kills = np.array([0])
self.prev_properties = None
self.properties = None
self.env.new_episode()
self._rgb_array = self.env.get_state().screen_buffer
observation = self._process_image(self._rgb_array)
return observation
def reset(self):
observation = self.sub_reset()
return observation
def sub_step(self, action):
"""Take step"""
one_hot_action = np.zeros(self.action_space.n, dtype=int)
one_hot_action[action] = 1
# ALWAYS SPRINTING
one_hot_action = np.append(one_hot_action, [1])
assert len(one_hot_action) == len(self.env.get_available_buttons())
_ = self.env.make_action(list(one_hot_action),
self._action_frame_repeat)
self.update_game_variables()
if self.steps > 1:
reward = self.update_reward()
else:
reward = 0
self.steps += 1
self.global_steps += 1
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(self._rgb_array)
return observation, reward, done
def step(self, action):
observation, reward, done = self.sub_step(action)
return observation, reward, done, {}
def close(self):
"""Close environment"""
self.env.close()
def seed(self, seed=None):
"""Seed"""
if seed:
self.env.set_seed(seed)
def render(self, mode='human'):
"""Render frame"""
if mode == 'rgb_array':
return self._rgb_array
raise NotImplementedError
class Vizdoom_env(object):
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_game(self):
self.game.init()
self.new_episode()
def new_episode(self, init_state=None):
self.game.new_episode()
if init_state is not None:
self.initialize_state(init_state)
self.take_action('NONE')
state = self.game.get_state()
if state is None:
raise RuntimeError('Cannot get initial states')
img_arr = np.transpose(state.screen_buffer.copy(), [1, 2, 0])
self.x_size = img_arr.shape[1]
self.y_size = img_arr.shape[0]
self.channel = img_arr.shape[2]
self.get_state()
if self.verbose:
self.call_all_perception_primitives()
p_v = self.get_perception_vector()
self.s_h = [img_arr.copy()]
self.a_h = []
self.p_v_h = [p_v.copy()] # perception vector
def end_game(self):
self.game.close()
def state_transition(self, action_string):
if action_string == 'NONE' or action_string in self.action_strings:
self.take_action(action_string)
self.a_h.append(action_string)
if self.verbose:
self.print_state()
if FRAME_SKIP[action_string][2] == 0:
self.get_state()
self.s_h.append(self.screen.copy())
p_v = self.get_perception_vector()
self.p_v_h.append(p_v.copy()) # perception vector
self.post_none(action_string)
if FRAME_SKIP[action_string][2] == 1:
self.get_state()
self.s_h.append(self.screen.copy())
p_v = self.get_perception_vector()
self.p_v_h.append(p_v.copy()) # perception vector
if self.verbose:
self.call_all_perception_primitives()
else:
raise ValueError('Unknown action')
def call_all_perception_primitives(self):
for actor in MONSTER_LIST + ITEMS_IN_INTEREST:
self.in_target(actor)
for dist in self.distance_dict.keys():
for horz in self.horizontal_dict.keys():
self.exist_actor_in_distance_horizontal(actor, dist, horz)
for weapon_slot in range(1, 10):
self.have_weapon(weapon_slot)
self.have_ammo(weapon_slot)
self.selected_weapon(weapon_slot)
for actor in MONSTER_LIST:
self.is_there(actor)
self.no_selected_weapon_ammo()
def take_action(self, action):
action_vector = [a == action for a in self.action_strings]
frame_skip = FRAME_SKIP[action][0]
if action == 'ATTACK':
state = self.game.get_state()
gv_values = dict(zip(self.game_variable_strings,
state.game_variables))
weapon_num = int(gv_values['SELECTED_WEAPON'])
frame_skip = ATTACK_FRAME_SKIP[weapon_num]
self.game.make_action(action_vector, frame_skip)
def post_none(self, action):
none_vector = [a == 'NONE' for a in self.action_strings]
self.game.make_action(none_vector, FRAME_SKIP[action][1])
def get_action_list(self):
return self.action_strings
def init_actors(self):
self.actors = {}
def check_and_add_to_actors(self, actor_name, label):
if actor_name not in self.actors:
self.actors[actor_name] = []
self.actors[actor_name].append(label)
def get_actor_by_name(self, actor_name):
if actor_name not in self.actors:
self.actors[actor_name] = []
return self.actors[actor_name]
def get_state(self):
state = self.game.get_state()
if state is None:
self.game_variables = dict()
self.player = None
self.monsters = []
self.ammo = []
self.init_actors()
return
self.game_variable_values = dict(zip(self.game_variable_strings, state.game_variables))
self.monsters = []
self.ammo = []
self.weapons = []
self.actors = {}
for l in state.labels:
if l.object_name in PLAYER_NAME:
self.player = l
elif l.object_name in MONSTER_LIST:
self.monsters.append(l)
self.check_and_add_to_actors(l.object_name, l)
else:
self.check_and_add_to_actors(l.object_name, l)
self.labels = state.labels
self.screen = np.transpose(state.screen_buffer, [1, 2, 0]).copy()
def get_perception_vector_cond(self):
if self.perception_type == 'simple' or \
self.perception_type == 'more_simple':
return self.get_perception_vector_cond_simple()
else:
return self.get_perception_vector_cond_basic()
def get_perception_vector_cond_basic(self):
vec = []
for dist in self.distance_dict.keys():
for horz in self.horizontal_dict.keys():
for actor in MONSTER_LIST + ITEMS_IN_INTEREST:
vec.append('EXIST {} IN {} {}'.format(actor, dist, horz))
for actor in MONSTER_LIST:
vec.append('INTARGET {}'.format(actor))
return vec
def get_perception_vector_cond_simple(self):
vec = []
for actor in MONSTER_LIST:
vec.append('ISTHERE {}'.format(actor))
if self.perception_type == 'more_simple':
return vec
for actor in MONSTER_LIST:
vec.append('INTARGET {}'.format(actor))
return vec
def get_perception_vector(self):
if self.perception_type == 'simple' or\
self.perception_type == 'more_simple':
return self.get_perception_vector_simple()
else: return self.get_perception_vector_basic()
def get_perception_vector_basic(self):
vec = []
for dist in self.distance_dict.keys():
for horz in self.horizontal_dict.keys():
for actor in MONSTER_LIST + ITEMS_IN_INTEREST:
vec.append(self.exist_actor_in_distance_horizontal(actor, dist, horz))
for actor in MONSTER_LIST:
vec.append(self.in_target(actor))
return np.array(vec)
def get_perception_vector_simple(self):
vec = []
for actor in MONSTER_LIST:
vec.append(self.is_there(actor))
if self.perception_type == 'more_simple':
return np.array(vec)
for actor in MONSTER_LIST:
vec.append(self.in_target(actor))
return np.array(vec)
def print_state(self):
state = self.game.get_state()
if state is None:
print('No state')
return
game_variables = dict(zip(self.game_variable_strings, state.game_variables))
game_variable_print = ''
for key in sorted(game_variables.keys()):
game_variable_print += '{}: {}, '.format(key, game_variables[key])
game_variable_print += '\n'
print(game_variable_print)
for l in state.labels:
print("id: {id}, name: {name}, position: [{pos_x},{pos_y},{pos_z}], "
"velocity: [{vel_x},{vel_y},{vel_z}], "
"angle: [{angle},{pitch},{roll}], "
"box: [{x},{y},{width},{height}]\n".format(
id=l.object_id, name=l.object_name,
pos_x=l.object_position_x, pos_y=l.object_position_y,
pos_z=l.object_position_z,
vel_x=l.object_velocity_x, vel_y=l.object_velocity_y,
vel_z=l.object_velocity_z,
angle=l.object_angle, pitch=l.object_pitch,
roll=l.object_roll,
x=l.x, y=l.y, width=l.width, height=l.height))
def is_there(self, actor):
if len(self.get_actor_by_name(actor)) > 0:
if self.verbose: print('ISTHERE {}'.format(actor))
return True
else: return False
def in_target(self, actor):
center_x = self.x_size / 2
center_y = self.y_size / 2
for a in self.get_actor_by_name(actor):
a_x_min, a_x_max = a.x, a.x + a.width
a_y_min, a_y_max = a.y, a.y + a.height
if center_x > a_x_min and center_x < a_x_max and\
center_y > a_y_min and center_y < a_y_max:
if self.verbose:
print('INTARGET {}'.format(actor))
return True
return False
def exist_actor_in_distance_horizontal(self, actor, dist, horz):
cen_x = self.x_size / 2
p = self.player
for a in self.get_actor_by_name(actor):
a_x_min, a_x_max = a.x, a.x + a.width
d_x = a.object_position_x - p.object_position_x
d_y = a.object_position_y - p.object_position_y
d = math.sqrt(d_x**2 + d_y**2)
if self.distance_dict[dist](d) and self.horizontal_dict[horz](a_x_min, a_x_max, cen_x):
if self.verbose:
print('EXIST {} in {} {}'.format(actor, dist, horz))
return True
return False
# Weapons
# 1: Fist, chainsaw, 2: pistol, 3: shotgun, 4: chaingun, 5: rocket launcher, 6: plazma rifle
# SELECT_WEAPON_1 switch between fist and chainsaw
def have_weapon(self, weapon_slot):
if self.game_variable_values['WEAPON{}'.format(weapon_slot)] > 0:
if self.verbose:
print('Have weapon {}'.format(weapon_slot))
return True
return False
def have_ammo(self, weapon_slot):
if weapon_slot == 1: # Fist or Chainsaw
if self.verbose:
print('Have ammo {}'.format(weapon_slot))
return True
if self.game_variable_values['AMMO{}'.format(weapon_slot)] > 0:
if self.verbose:
print('Have ammo {}'.format(weapon_slot))
return True
return False
def selected_weapon(self, weapon_slot):
if self.game_variable_values['SELECTED_WEAPON'] == weapon_slot:
if self.verbose:
print('Weapon {} is selected'.format(weapon_slot))
return True
return False
def no_selected_weapon_ammo(self):
if self.game_variable_values['SELECTED_WEAPON_AMMO'] == 0:
if self.verbose:
print('no selected weapon ammo is left')
return True
return False
def initialize_state(self, init_state):
""" Takes random arguments and initialies the state
Assumes that the max number of monster and ammo spawns is 5
Params:
init_state [{"player_pos": [x, y], "monster_pos": [[x1, y1], [x2, y2]]}]
"""
if 'player_pos' in init_state:
x, y = init_state['player_pos']
self.game.send_game_command('puke 20 {} {}'.format(x, y))
if 'demon_pos' in init_state:
for i, (x, y) in enumerate(init_state['demon_pos']):
self.game.send_game_command(
'puke {} {} {}'.format(21 + i, x, y))
if 'revenant_pos' in init_state:
for i, (x, y) in enumerate(init_state['revenant_pos']):
self.game.send_game_command(
'puke {} {} {}'.format(5 + i, x, y))
if 'hellknight_pos' in init_state:
for i, (x, y) in enumerate(init_state['hellknight_pos']):
self.game.send_game_command(
'puke {} {} {}'.format(15 + i, x, y))
if 'ammo_pos' in init_state:
for i, (x, y) in enumerate(init_state['ammo_pos']):
self.game.send_game_command(
'puke {} {} {}'.format(10 + i, x, y))
