def __init__(self, environment_filename=None, docker_training=False, worker_id=0, retro=True,
timeout_wait=30, realtime_mode=False):
"""
Arguments:
environment_filename: The file path to the Unity executable. Does not require the extension.
docker_training: Whether this is running within a docker environment and should use a virtual
frame buffer (xvfb).
worker_id: The index of the worker in the case where multiple environments are running. Each
environment reserves port (5005 + worker_id) for communication with the Unity executable.
retro: Resize visual observation to 84x84 (int8) and flattens action space.
timeout_wait: Time for python interface to wait for environment to connect.
realtime_mode: Whether to render the environment window image and run environment at realtime.
"""
if self.is_grading():
environment_filename = None
docker_training = True
self._env = UnityEnvironment(environment_filename,
worker_id,
docker_training=docker_training,
timeout_wait=timeout_wait)
split_name = self._env.academy_name.split('-v')
if len(split_name) == 2 and split_name[0] == "ObstacleTower":
self.name, self.version = split_name
else:
raise UnityGymException(
"Attempting to launch non-Obstacle Tower environment"
)
if self.version not in self.ALLOWED_VERSIONS:
raise UnityGymException(
"Invalid Obstacle Tower version. Your build is v" + self.version +
" but only the following versions are compatible with this gym: " +
str(self.ALLOWED_VERSIONS)
)
self.visual_obs = None
self._current_state = None
self._n_agents = None
self._done_grading = False
self._flattener = None
self._seed = None
self._floor = None
self.realtime_mode = realtime_mode
self.game_over = False # Hidden flag used by Atari environments to determine if the game is over
self.retro = retro
flatten_branched = self.retro
uint8_visual = self.retro
# Check brain configuration
if len(self._env.brains) != 1:
raise UnityGymException(
"There can only be one brain in a UnityEnvironment "
"if it is wrapped in a gym.")
self.brain_name = self._env.external_brain_names[0]
brain = self._env.brains[self.brain_name]
if brain.number_visual_observations == 0:
raise UnityGymException("Environment provides no visual observations.")
self.uint8_visual = uint8_visual
if brain.number_visual_observations > 1:
logger.warning("The environment contains more than one visual observation. "
"Please note that only the first will be provided in the observation.")
# Check for number of agents in scene.
initial_info = self._env.reset(train_mode=not self.realtime_mode)[self.brain_name]
self._check_agents(len(initial_info.agents))
# Set observation and action spaces
if len(brain.vector_action_space_size) == 1:
self._action_space = spaces.Discrete(brain.vector_action_space_size[0])
else:
if flatten_branched:
self._flattener = ActionFlattener(brain.vector_action_space_size)
self._action_space = self._flattener.action_space
else:
self._action_space = spaces.MultiDiscrete(brain.vector_action_space_size)
high = np.array([np.inf] * brain.vector_observation_space_size)
self.action_meanings = brain.vector_action_descriptions
depth = 3
image_space_max = 1.0
image_space_dtype = np.float32
camera_height = brain.camera_resolutions[0]["height"]
camera_width = brain.camera_resolutions[0]["width"]
if self.retro:
image_space_max = 255
image_space_dtype = np.uint8
camera_height = 84
camera_width = 84
image_space = spaces.Box(
0, image_space_max,
dtype=image_space_dtype,
shape=(camera_height, camera_width, depth)
)
if self.retro:
self._observation_space = image_space
else:
max_float = np.finfo(np.float32).max
keys_space = spaces.Discrete(5)
time_remaining_space = spaces.Box(low=0.0, high=max_float, shape=(1,), dtype=np.float32)
self._observation_space = spaces.Tuple(
(image_space, keys_space, time_remaining_space)
)
class ObstacleTowerEnv(gym.Env):
ALLOWED_VERSIONS = ['1', '1.1', '1.2']
def __init__(self, environment_filename=None, docker_training=False, worker_id=0, retro=True,
timeout_wait=30, realtime_mode=False):
"""
Arguments:
environment_filename: The file path to the Unity executable. Does not require the extension.
docker_training: Whether this is running within a docker environment and should use a virtual
frame buffer (xvfb).
worker_id: The index of the worker in the case where multiple environments are running. Each
environment reserves port (5005 + worker_id) for communication with the Unity executable.
retro: Resize visual observation to 84x84 (int8) and flattens action space.
timeout_wait: Time for python interface to wait for environment to connect.
realtime_mode: Whether to render the environment window image and run environment at realtime.
"""
if self.is_grading():
environment_filename = None
docker_training = True
self._env = UnityEnvironment(environment_filename,
worker_id,
docker_training=docker_training,
timeout_wait=timeout_wait)
split_name = self._env.academy_name.split('-v')
if len(split_name) == 2 and split_name[0] == "ObstacleTower":
self.name, self.version = split_name
else:
raise UnityGymException(
"Attempting to launch non-Obstacle Tower environment"
)
if self.version not in self.ALLOWED_VERSIONS:
raise UnityGymException(
"Invalid Obstacle Tower version. Your build is v" + self.version +
" but only the following versions are compatible with this gym: " +
str(self.ALLOWED_VERSIONS)
)
self.visual_obs = None
self._current_state = None
self._n_agents = None
self._done_grading = False
self._flattener = None
self._seed = None
self._floor = None
self.realtime_mode = realtime_mode
self.game_over = False # Hidden flag used by Atari environments to determine if the game is over
self.retro = retro
flatten_branched = self.retro
uint8_visual = self.retro
# Check brain configuration
if len(self._env.brains) != 1:
raise UnityGymException(
"There can only be one brain in a UnityEnvironment "
"if it is wrapped in a gym.")
self.brain_name = self._env.external_brain_names[0]
brain = self._env.brains[self.brain_name]
if brain.number_visual_observations == 0:
raise UnityGymException("Environment provides no visual observations.")
self.uint8_visual = uint8_visual
if brain.number_visual_observations > 1:
logger.warning("The environment contains more than one visual observation. "
"Please note that only the first will be provided in the observation.")
# Check for number of agents in scene.
initial_info = self._env.reset(train_mode=not self.realtime_mode)[self.brain_name]
self._check_agents(len(initial_info.agents))
# Set observation and action spaces
if len(brain.vector_action_space_size) == 1:
self._action_space = spaces.Discrete(brain.vector_action_space_size[0])
else:
if flatten_branched:
self._flattener = ActionFlattener(brain.vector_action_space_size)
self._action_space = self._flattener.action_space
else:
self._action_space = spaces.MultiDiscrete(brain.vector_action_space_size)
high = np.array([np.inf] * brain.vector_observation_space_size)
self.action_meanings = brain.vector_action_descriptions
depth = 3
image_space_max = 1.0
image_space_dtype = np.float32
camera_height = brain.camera_resolutions[0]["height"]
camera_width = brain.camera_resolutions[0]["width"]
if self.retro:
image_space_max = 255
image_space_dtype = np.uint8
camera_height = 84
camera_width = 84
image_space = spaces.Box(
0, image_space_max,
dtype=image_space_dtype,
shape=(camera_height, camera_width, depth)
)
if self.retro:
self._observation_space = image_space
else:
max_float = np.finfo(np.float32).max
keys_space = spaces.Discrete(5)
time_remaining_space = spaces.Box(low=0.0, high=max_float, shape=(1,), dtype=np.float32)
self._observation_space = spaces.Tuple(
(image_space, keys_space, time_remaining_space)
)
def done_grading(self):
return self._done_grading
def is_grading(self):
return os.getenv('OTC_EVALUATION_ENABLED', False)
def reset(self):
"""Resets the state of the environment and returns an initial observation.
In the case of multi-agent environments, this is a list.
Returns: observation (object/list): the initial observation of the
space.
"""
reset_params = {}
if self._floor is not None:
reset_params['floor-number'] = self._floor
if self._seed is not None:
reset_params['tower-seed'] = self._seed
info = self._env.reset(config=reset_params,
train_mode=not self.realtime_mode)[self.brain_name]
n_agents = len(info.agents)
self._check_agents(n_agents)
self.game_over = False
obs, reward, done, info = self._single_step(info)
return obs
def step(self, action):
"""Run one timestep of the environment's dynamics. When end of
episode is reached, you are responsible for calling `reset()`
to reset this environment's state.
Accepts an action and returns a tuple (observation, reward, done, info).
In the case of multi-agent environments, these are lists.
Args:
action (object/list): an action provided by the environment
Returns:
observation (object/list): agent's observation of the current environment
reward (float/list) : amount of reward returned after previous action
done (boolean/list): whether the episode has ended.
info (dict): contains auxiliary diagnostic information, including BrainInfo.
"""
# Use random actions for all other agents in environment.
if self._flattener is not None:
# Translate action into list
action = self._flattener.lookup_action(action)
info = self._env.step(action)[self.brain_name]
n_agents = len(info.agents)
self._check_agents(n_agents)
self._current_state = info
obs, reward, done, info = self._single_step(info)
self.game_over = done
if info.get('text_observation') == 'evaluation_complete':
done = True
self._done_grading = True
return obs, reward, done, info
def _single_step(self, info):
self.visual_obs = self._preprocess_single(info.visual_observations[0][0, :, :, :])
if self.retro:
self.visual_obs = self._resize_observation(self.visual_obs)
self.visual_obs = self._add_stats_to_image(
self.visual_obs, info.vector_observations[0])
default_observation = self.visual_obs
else:
default_observation = self._prepare_tuple_observation(
self.visual_obs, info.vector_observations[0])
return default_observation, info.rewards[0], info.local_done[0], {
"text_observation": info.text_observations[0],
"brain_info": info}
def _preprocess_single(self, single_visual_obs):
if self.uint8_visual:
return (255.0 * single_visual_obs).astype(np.uint8)
else:
return single_visual_obs
def render(self, mode='rgb_array'):
return self.visual_obs
def close(self):
"""Override _close in your subclass to perform any necessary cleanup.
Environments will automatically close() themselves when
garbage collected or when the program exits.
"""
self._env.close()
if self.is_grading():
import time
while True:
time.sleep(10)
def get_action_meanings(self):
return self.action_meanings
def seed(self, seed=None):
"""Sets a fixed seed for this env's random number generator(s).
The valid range for seeds is [0, 100). By default a random seed
will be chosen.
"""
if seed is None:
self._seed = seed
return
seed = int(seed)
if seed < 0 or seed >= 100:
logger.warn(
"Seed outside of valid range [0, 100). A random seed "
"within the valid range will be used on next reset."
)
logger.warn("New seed " + str(seed) + " will apply on next reset.")
self._seed = seed
def floor(self, floor=None):
"""Sets the starting floor to a fixed floor number on subsequent environment
resets."""
if floor is None:
self._floor = floor
return
floor = int(floor)
if floor < 0 or floor >= 25:
logger.warn(
"Starting floor outside of valid range [0, 25). Floor 0 will be used"
"on next reset."
)
logger.warn("New starting floor " + str(floor) + " will apply on next reset.")
self._floor = floor
@staticmethod
def _resize_observation(observation):
"""
Re-sizes visual observation to 84x84
"""
obs_image = Image.fromarray(observation)
obs_image = obs_image.resize((84, 84), Image.NEAREST)
return np.array(obs_image)
@staticmethod
def _prepare_tuple_observation(vis_obs, vector_obs):
"""
Converts separate visual and vector observation into prepared tuple
"""
key = vector_obs[0:6]
time = vector_obs[6]
key_num = np.argmax(key, axis=0)
return vis_obs, key_num, time
@staticmethod
def _add_stats_to_image(vis_obs, vector_obs):
"""
Displays time left and number of keys on visual observation
"""
key = vector_obs[0:6]
time = vector_obs[6]
key_num = np.argmax(key, axis=0)
time_num = min(time, 10000) / 10000
vis_obs[0:10, :, :] = 0
for i in range(key_num):
start = int(i * 16.8) + 4
end = start + 10
vis_obs[1:5, start:end, 0:2] = 255
vis_obs[6:10, 0:int(time_num * 84), 1] = 255
return vis_obs
def _check_agents(self, n_agents):
if n_agents > 1:
raise UnityGymException(
"The environment was launched as a single-agent environment, however"
"there is more than one agent in the scene.")
if self._n_agents is None:
self._n_agents = n_agents
logger.info("{} agents within environment.".format(n_agents))
elif self._n_agents != n_agents:
raise UnityGymException("The number of agents in the environment has changed since "
"initialization. This is not supported.")
@property
def metadata(self):
return {'render.modes': ['rgb_array']}
@property
def reward_range(self):
return -float('inf'), float('inf')
@property
def spec(self):
return None
@property
def action_space(self):
return self._action_space
@property
def observation_space(self):
return self._observation_space
@property
def number_agents(self):
return self._n_agents