def test_measurements_observation_space():
# empty measurements space
measurements_space = VectorObservationSpace(0)
# vector space
measurements_space = VectorObservationSpace(
3, measurements_names=['a', 'b', 'c'])
def get_observation_space(sensor):
'''Creates the observation space for the given sensor
sensor - String with the desired sensor to add to the
observation space
'''
obs = StateSpace({})
if not isinstance(sensor, str):
raise GenericError("None string type for sensor type: {}".format(
type(sensor)))
if sensor == Input.CAMERA.value or sensor == Input.OBSERVATION.value or \
sensor == Input.LEFT_CAMERA.value:
obs[sensor] = ImageObservationSpace(shape=np.array(
(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 3)),
high=255,
channels_axis=-1)
elif sensor == Input.STEREO.value:
obs[sensor] = PlanarMapsObservationSpace(shape=np.array(
(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 2)),
low=0,
high=255,
channels_axis=-1)
elif sensor == Input.LIDAR.value:
obs[sensor] = VectorObservationSpace(shape=TRAINING_LIDAR_SIZE,
low=0.15,
high=1.0)
elif sensor == Input.SECTOR_LIDAR.value:
obs[sensor] = VectorObservationSpace(shape=TRAINING_LIDAR_SIZE,
low=0.15,
high=SECTOR_LIDAR_CLIPPING_DIST)
else:
raise Exception(
"Unable to set observation space for sensor {}".format(sensor))
return obs
def __init__(self, level: LevelSelection, seed: int, frame_skip: int,
human_control: bool, custom_reward_threshold: Union[int,
float],
visualization_parameters: VisualizationParameters, **kwargs):
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters,
**kwargs)
self.state_space = StateSpace({
"observation":
VectorObservationSpace(shape=4)
# "observation": PlanarMapsObservationSpace(shape=np.array([84, 84, 2]), low=0, high=1)
# "observation": TensorObservationSpace(shape=np.array([2, 2]), low=0, high=1)
# "player": VectorObservationSpace(shape=2),
# "map": PlanarMapsObservationSpace(shape=np.array([84, 84, 1]), low=0, high=2),
})
self.goal_space = VectorObservationSpace(shape=4)
# self.state_space = PlanarMapsObservationSpace(shape=np.array([84, 84, 2]), low=0, high=1)
self.action_space = DiscreteActionSpace(num_actions=4,
descriptions={
"0": "up",
"1": "down",
"2": "left",
"3": "right"
})
self.env = FacEnv()
self.reward_limit = -np.power(
np.sum(
np.arange(1 + np.abs(self.env.target['x']) +
np.abs(self.env.target['y']))) / 2, 2)
def get_filtered_observation_space(
self, input_observation_space: VectorObservationSpace
) -> ObservationSpace:
self.measurement_names = copy.copy(
input_observation_space.measurements_names)
if self.reduction_method == self.ReductionMethod.Keep:
input_observation_space.shape[-1] = len(self.part_names)
self.indices_to_keep = [
idx for idx, val in enumerate(self.measurement_names)
if val in self.part_names
]
input_observation_space.measurements_names = copy.copy(
self.part_names)
elif self.reduction_method == self.ReductionMethod.Discard:
input_observation_space.shape[-1] -= len(self.part_names)
self.indices_to_keep = [
idx for idx, val in enumerate(self.measurement_names)
if val not in self.part_names
]
input_observation_space.measurements_names = [
val for val in input_observation_space.measurements_names
if val not in self.part_names
]
else:
raise ValueError("The given reduction method is not supported")
return input_observation_space
def set_environment_parameters(self, spaces: SpacesDefinition):
"""
Sets the parameters that are environment dependent. As a side effect, initializes all the components that are
dependent on those values, by calling init_environment_dependent_modules
:param spaces: the environment spaces definition
:return: None
"""
self.spaces = copy.deepcopy(spaces)
if self.ap.algorithm.use_accumulated_reward_as_measurement:
if 'measurements' in self.spaces.state.sub_spaces:
self.spaces.state['measurements'].shape += 1
self.spaces.state['measurements'].measurements_names += ['accumulated_reward']
else:
self.spaces.state['measurements'] = VectorObservationSpace(1, measurements_names=['accumulated_reward'])
for observation_name in self.spaces.state.sub_spaces.keys():
self.spaces.state[observation_name] = \
self.pre_network_filter.get_filtered_observation_space(observation_name,
self.input_filter.get_filtered_observation_space(observation_name,
self.spaces.state[observation_name]))
self.spaces.reward = self.pre_network_filter.get_filtered_reward_space(
self.input_filter.get_filtered_reward_space(self.spaces.reward))
self.spaces.action = self.output_filter.get_unfiltered_action_space(self.spaces.action)
if isinstance(self.in_action_space, GoalsSpace):
# TODO: what if the goal type is an embedding / embedding change?
self.spaces.goal = self.in_action_space
self.spaces.goal.set_target_space(self.spaces.state[self.spaces.goal.goal_name])
self.init_environment_dependent_modules()
def set_environment_parameters(self, spaces: SpacesDefinition):
self.spaces = copy.deepcopy(spaces)
self.spaces.goal = VectorObservationSpace(
shape=self.spaces.state['measurements'].shape,
measurements_names=self.spaces.state['measurements'].
measurements_names)
# if the user has filled some scale values, check that he got the names right
if set(self.spaces.state['measurements'].measurements_names).intersection(
self.ap.algorithm.scale_measurements_targets.keys()) !=\
set(self.ap.algorithm.scale_measurements_targets.keys()):
raise ValueError(
"Some of the keys in parameter scale_measurements_targets ({}) are not defined in "
"the measurements space {}".format(
self.ap.algorithm.scale_measurements_targets.keys(),
self.spaces.state['measurements'].measurements_names))
super().set_environment_parameters(self.spaces)
# the below is done after calling the base class method, as it might add accumulated reward as a measurement
# fill out the missing measurements scale factors
for measurement_name in self.spaces.state[
'measurements'].measurements_names:
if measurement_name not in self.ap.algorithm.scale_measurements_targets:
self.ap.algorithm.scale_measurements_targets[
measurement_name] = 1
self.target_measurements_scale_factors = \
np.array([self.ap.algorithm.scale_measurements_targets[measurement_name] for measurement_name in
self.spaces.state['measurements'].measurements_names])
def _setup_state_space(self):
state_space = StateSpace({})
dummy_obs = self._process_observation(self.env.observation_spec())
state_space['measurements'] = VectorObservationSpace(dummy_obs['measurements'].shape[0])
if self.base_parameters.use_camera_obs:
state_space['camera'] = PlanarMapsObservationSpace(dummy_obs['camera'].shape, 0, 255)
return state_space
def get_filtered_observation_space(self, input_observation_space: ObservationSpace) -> ObservationSpace:
if isinstance(input_observation_space, VectorObservationSpace):
self.input_observation_space = input_observation_space = VectorObservationSpace(input_observation_space.shape * self.stack_size)
else:
if self.stacking_axis == -1:
input_observation_space.shape = np.append(input_observation_space.shape, values=[self.stack_size], axis=0)
else:
input_observation_space.shape = np.insert(input_observation_space.shape, obj=self.stacking_axis,
values=[self.stack_size], axis=0)
return input_observation_space
def get_observation_space(sensor, model_metadata=None):
'''Creates the observation space for the given sensor
sensor - String with the desired sensor to add to the
observation space
model_metadata - model metadata information
'''
obs = StateSpace({})
if not isinstance(sensor, str):
raise GenericError("None string type for sensor type: {}".format(
type(sensor)))
if sensor == Input.CAMERA.value or sensor == Input.OBSERVATION.value or \
sensor == Input.LEFT_CAMERA.value:
obs[sensor] = ImageObservationSpace(shape=np.array(
(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 3)),
high=255,
channels_axis=-1)
elif sensor == Input.STEREO.value:
obs[sensor] = PlanarMapsObservationSpace(shape=np.array(
(TRAINING_IMAGE_SIZE[1], TRAINING_IMAGE_SIZE[0], 2)),
low=0,
high=255,
channels_axis=-1)
elif sensor == Input.LIDAR.value:
obs[sensor] = VectorObservationSpace(shape=TRAINING_LIDAR_SIZE,
low=0.15,
high=1.0)
elif sensor == Input.SECTOR_LIDAR.value:
obs[sensor] = VectorObservationSpace(shape=NUMBER_OF_LIDAR_SECTORS,
low=0.0,
high=1.0)
elif sensor == Input.DISCRETIZED_SECTOR_LIDAR.value:
lidar_config = model_metadata[ModelMetadataKeys.LIDAR_CONFIG.value]
shape = lidar_config[ModelMetadataKeys.NUM_SECTORS.value] * \
lidar_config[ModelMetadataKeys.NUM_VALUES_PER_SECTOR.value]
obs[sensor] = VectorObservationSpace(shape=shape, low=0.0, high=1.0)
else:
raise Exception(
"Unable to set observation space for sensor {}".format(sensor))
return obs
def test_get_filtered_observation_space():
# Keep
observation_space = VectorObservationSpace(
3, measurements_names=['a', 'b', 'c'])
env_response = EnvResponse(next_state={'observation': np.ones([3])},
reward=0,
game_over=False)
reduction_filter = InputFilter()
reduction_filter.add_observation_filter(
'observation', 'reduce',
ObservationReductionBySubPartsNameFilter(
["a"],
ObservationReductionBySubPartsNameFilter.ReductionMethod.Keep))
filtered_observation_space = reduction_filter.get_filtered_observation_space(
'observation', observation_space)
assert np.all(filtered_observation_space.shape == np.array([1]))
assert filtered_observation_space.measurements_names == ['a']
# Discard
observation_space = VectorObservationSpace(
3, measurements_names=['a', 'b', 'c'])
env_response = EnvResponse(next_state={'observation': np.ones([3])},
reward=0,
game_over=False)
reduction_filter = InputFilter()
reduction_filter.add_observation_filter(
'observation', 'reduce',
ObservationReductionBySubPartsNameFilter(
["a"],
ObservationReductionBySubPartsNameFilter.ReductionMethod.Discard))
filtered_observation_space = reduction_filter.get_filtered_observation_space(
'observation', observation_space)
assert np.all(filtered_observation_space.shape == np.array([2]))
assert filtered_observation_space.measurements_names == ['b', 'c']
def test_filter():
# Keep
observation_space = VectorObservationSpace(
3, measurements_names=['a', 'b', 'c'])
env_response = EnvResponse(next_state={'observation': np.ones([3])},
reward=0,
game_over=False)
reduction_filter = InputFilter()
reduction_filter.add_observation_filter(
'observation', 'reduce',
ObservationReductionBySubPartsNameFilter(
["a"],
ObservationReductionBySubPartsNameFilter.ReductionMethod.Keep))
reduction_filter.get_filtered_observation_space('observation',
observation_space)
result = reduction_filter.filter(env_response)[0]
unfiltered_observation = env_response.next_state['observation']
filtered_observation = result.next_state['observation']
# make sure the original observation is unchanged
assert unfiltered_observation.shape == (3, )
# validate the shape of the filtered observation
assert filtered_observation.shape == (1, )
# Discard
reduction_filter = InputFilter()
reduction_filter.add_observation_filter(
'observation', 'reduce',
ObservationReductionBySubPartsNameFilter(
["a"],
ObservationReductionBySubPartsNameFilter.ReductionMethod.Discard))
reduction_filter.get_filtered_observation_space('observation',
observation_space)
result = reduction_filter.filter(env_response)[0]
unfiltered_observation = env_response.next_state['observation']
filtered_observation = result.next_state['observation']
# make sure the original observation is unchanged
assert unfiltered_observation.shape == (3, )
# validate the shape of the filtered observation
assert filtered_observation.shape == (2, )
def train_on_csv_file(csv_file, n_epochs, dataset_size, obs_dim, act_dim):
tf.reset_default_graph(
) # just to clean things up; only needed for the tutorial
schedule_params = set_schedule_params(n_epochs, dataset_size)
#########
# Agent #
#########
# note that we have moved to BCQ, which will help the training to converge better and faster
agent_params = set_agent_params(DDQNBCQAgentParameters)
# additional setting for DDQNBCQAgentParameters agent parameters
# can use either a kNN or a NN based model for predicting which actions not to max over in the bellman equation
# agent_params.algorithm.action_drop_method_parameters = KNNParameters()
agent_params.algorithm.action_drop_method_parameters = NNImitationModelParameters(
)
DATATSET_PATH = csv_file
agent_params.memory.load_memory_from_file_path = CsvDataset(
DATATSET_PATH, is_episodic=True)
spaces = SpacesDefinition(state=StateSpace(
{'observation': VectorObservationSpace(shape=obs_dim)}),
goal=None,
action=DiscreteActionSpace(act_dim),
reward=RewardSpace(1))
graph_manager = BatchRLGraphManager(
agent_params=agent_params,
env_params=None,
spaces_definition=spaces,
schedule_params=schedule_params,
vis_params=VisualizationParameters(
dump_signals_to_csv_every_x_episodes=1),
reward_model_num_epochs=30,
train_to_eval_ratio=0.4)
graph_manager.create_graph(task_parameters)
graph_manager.improve()
return
def __init__(
self,
level: LevelSelection,
seed: int,
frame_skip: int,
human_control: bool,
custom_reward_threshold: Union[int, float],
visualization_parameters: VisualizationParameters,
host: str,
port: int,
timeout: float,
number_of_vehicles: int,
number_of_walkers: int,
weather_id: int, #rendering_mode: bool,
ego_vehicle_filter: str,
display_size: int,
sensors: List[SensorTypes],
camera_height: int,
camera_width: int,
lidar_bin: float,
obs_range: float,
display_route: bool,
render_pygame: bool,
d_behind: float,
out_lane_thres: float,
desired_speed: float,
max_past_step: int,
dt: float,
discrete: bool,
discrete_acc: List[float],
discrete_steer: List[float],
continuous_accel_range: List[float],
continuous_steer_range: List[float],
max_ego_spawn_times: int,
max_time_episode: int,
max_waypt: int,
**kwargs):
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters)
self.level = level
# self.frame_skip = frame_skip
# self.seed = seed
# self.human_control = human_control
# self.custom_reward_threshold = custom_reward_threshold
# self.visualization_paramters = visualization_parameters
self.host = host
self.port = port
self.timeout = timeout
self.number_of_vehicles = number_of_vehicles
self.number_of_walkers = number_of_walkers
self.weather_id = weather_id
self.ego_vehicle_filter = ego_vehicle_filter
self.display_size = display_size
self.sensors = sensors
self.camera_height = camera_height
self.camera_width = camera_width
self.obs_range = obs_range
self.lidar_bin = lidar_bin
self.obs_size = int(self.obs_range / self.lidar_bin)
self.display_route = display_route
self.render_pygame = render_pygame
self.d_behind = d_behind
self.out_lane_thres = out_lane_thres
self.desired_speed = desired_speed
self.max_past_step = max_past_step
self.dt = dt
self.discrete = discrete
self.discrete_acc = discrete_acc
self.discrete_steer = discrete_steer
self.continuous_accel_range = continuous_accel_range
self.continuous_steer_range = continuous_steer_range
self.max_ego_spawn_times = max_ego_spawn_times
self.max_time_episode = max_time_episode
self.max_waypt = max_waypt
# Connect to carla server and get world object
print('connecting to Carla server...')
self.client = carla.Client(self.host, self.port)
self.client.set_timeout(self.timeout)
self.traffic_manager = self.client.get_trafficmanager()
self.world = self.client.load_world(level)
print('Carla server connected!')
# Set weather
self.world.set_weather(CARLA_WEATHER_PRESETS[self.weather_id])
# Get spawn points
self._get_spawn_points()
# Create the ego vehicle blueprint
self.ego_bp = self._create_vehicle_bluepprint(self.ego_vehicle_filter,
color='49,8,8')
# Collision sensor
self.collision_hist = [] # The collision history
self.collision_hist_l = 1 # collision history length
self.collision_bp = self.world.get_blueprint_library().find(
'sensor.other.collision')
# Lidar sensor
self.lidar_data = None
self.lidar_height = 2.1
self.lidar_trans = carla.Transform(
carla.Location(x=0.0, z=self.lidar_height))
self.lidar_bp = self.world.get_blueprint_library().find(
'sensor.lidar.ray_cast')
self.lidar_bp.set_attribute('channels', '32')
self.lidar_bp.set_attribute('range', '5000')
# Camera sensor
self.camera_img = np.zeros((self.camera_height, self.camera_width, 3),
dtype=np.uint8)
self.camera_trans = carla.Transform(carla.Location(x=0.8, z=1.7))
self.camera_bp = self.world.get_blueprint_library().find(
'sensor.camera.rgb')
# Modify the attributes of the blueprint to set image resolution and field of view.
self.camera_bp.set_attribute('image_size_x', str(self.camera_width))
self.camera_bp.set_attribute('image_size_y', str(self.camera_height))
self.camera_bp.set_attribute('fov', '110')
# Set the time in seconds between sensor captures
self.camera_bp.set_attribute('sensor_tick', '0.02')
# Set fixed simulation step for synchronous mode
self.settings = self.world.get_settings()
self.settings.fixed_delta_seconds = self.dt
#self.settings.no_rendering_mode = rendering_mode
self._set_synchronous_mode(True)
# Record the time of total steps and resetting steps
self.reset_step = 0
self.total_step = 0
# Action space
if self.discrete:
self.discrete_act = [discrete_acc, discrete_steer]
self.n_acc = len(self.discrete_act[0])
self.n_steer = len(self.discrete_act[1])
self.action_space = DiscreteActionSpace(
num_actions=self.n_acc * self.n_steer,
descriptions=["acceleration", "steering"])
else:
self.action_space = BoxActionSpace(
shape=2,
low=np.array(
[continuous_accel_range[0], continuous_steer_range[0]]),
high=np.array(
[continuous_accel_range[1], continuous_steer_range[1]]),
descriptions=["acceleration", "steering"])
# Observation space
self.state_space = StateSpace({
"measurements":
VectorObservationSpace(shape=4,
low=np.array([-2, -1, -5, 0]),
high=np.array([2, 1, 30, 1]),
measurements_names=[
"lat_dist", "heading_error",
"ego_speed", "safety_margin"
])
})
if SensorTypes.FRONT_CAMERA in self.sensors:
self.state_space[
SensorTypes.FRONT_CAMERA.value] = ImageObservationSpace(
shape=np.array([self.camera_height, self.camera_width, 3]),
high=255)
if SensorTypes.LIDAR in self.sensors:
self.state_space[
SensorTypes.LIDAR.value] = PlanarMapsObservationSpace(
shape=np.array([self.obs_size, self.obs_size, 3]),
low=0,
high=255)
if SensorTypes.BIRDEYE in self.sensors:
self.state_space[
SensorTypes.BIRDEYE.value] = ImageObservationSpace(
shape=np.array([self.obs_size, self.obs_size, 3]),
high=255)
# Initialize the renderer
self._init_renderer()
self.reset_internal_state(True)
# ER - we'll be needing an episodic replay buffer for off-policy evaluation
agent_params.memory = EpisodicExperienceReplayParameters()
# E-Greedy schedule - there is no exploration in Batch RL. Disabling E-Greedy.
agent_params.exploration.epsilon_schedule = LinearSchedule(initial_value=0, final_value=0, decay_steps=1)
agent_params.exploration.evaluation_epsilon = 0
# can use either a kNN or a NN based model for predicting which actions not to max over in the bellman equation
#agent_params.algorithm.action_drop_method_parameters = KNNParameters()
DATATSET_PATH = 'acrobot_dataset.csv'
agent_params.memory = EpisodicExperienceReplayParameters()
agent_params.memory.load_memory_from_file_path = CsvDataset(DATATSET_PATH, is_episodic = True)
spaces = SpacesDefinition(state=StateSpace({'observation': VectorObservationSpace(shape=6)}),
goal=None,
action=DiscreteActionSpace(3),
reward=RewardSpace(1))
graph_manager = BatchRLGraphManager(agent_params=agent_params,
env_params=None,
spaces_definition=spaces,
schedule_params=schedule_params,
vis_params=VisualizationParameters(dump_signals_to_csv_every_x_episodes=1),
reward_model_num_epochs=30,
train_to_eval_ratio=0.4)
graph_manager.create_graph(task_parameters)
graph_manager.improve()
def __init__(self,
level: LevelSelection,
frame_skip: int,
visualization_parameters: VisualizationParameters,
target_success_rate: float = 1.0,
additional_simulator_parameters: Dict[str, Any] = {},
seed: Union[None, int] = None,
human_control: bool = False,
custom_reward_threshold: Union[int, float] = None,
random_initialization_steps: int = 1,
max_over_num_frames: int = 1,
observation_space_type: ObservationSpaceType = None,
**kwargs):
"""
:param level: (str)
A string representing the gym level to run. This can also be a LevelSelection object.
For example, BreakoutDeterministic-v0
:param frame_skip: (int)
The number of frames to skip between any two actions given by the agent. The action will be repeated
for all the skipped frames.
:param visualization_parameters: (VisualizationParameters)
The parameters used for visualizing the environment, such as the render flag, storing videos etc.
:param additional_simulator_parameters: (Dict[str, Any])
Any additional parameters that the user can pass to the Gym environment. These parameters should be
accepted by the __init__ function of the implemented Gym environment.
:param seed: (int)
A seed to use for the random number generator when running the environment.
:param human_control: (bool)
A flag that allows controlling the environment using the keyboard keys.
:param custom_reward_threshold: (float)
Allows defining a custom reward that will be used to decide when the agent succeeded in passing the environment.
If not set, this value will be taken from the Gym environment definition.
:param random_initialization_steps: (int)
The number of random steps that will be taken in the environment after each reset.
This is a feature presented in the DQN paper, which improves the variability of the episodes the agent sees.
:param max_over_num_frames: (int)
This value will be used for merging multiple frames into a single frame by taking the maximum value for each
of the pixels in the frame. This is particularly used in Atari games, where the frames flicker, and objects
can be seen in one frame but disappear in the next.
:param observation_space_type:
This value will be used for generating observation space. Allows a custom space. Should be one of
ObservationSpaceType. If not specified, observation space is inferred from the number of dimensions
of the observation: 1D: Vector space, 3D: Image space if 1 or 3 channels, PlanarMaps space otherwise.
"""
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters,
target_success_rate)
self.random_initialization_steps = random_initialization_steps
self.max_over_num_frames = max_over_num_frames
self.additional_simulator_parameters = additional_simulator_parameters
# hide warnings
gym.logger.set_level(40)
"""
load and initialize environment
environment ids can be defined in 3 ways:
1. Native gym environments like BreakoutDeterministic-v0 for example
2. Custom gym environments written and installed as python packages.
This environments should have a python module with a class inheriting gym.Env, implementing the
relevant functions (_reset, _step, _render) and defining the observation and action space
For example: my_environment_package:MyEnvironmentClass will run an environment defined in the
MyEnvironmentClass class
3. Custom gym environments written as an independent module which is not installed.
This environments should have a python module with a class inheriting gym.Env, implementing the
relevant functions (_reset, _step, _render) and defining the observation and action space.
For example: path_to_my_environment.sub_directory.my_module:MyEnvironmentClass will run an
environment defined in the MyEnvironmentClass class which is located in the module in the relative path
path_to_my_environment.sub_directory.my_module
"""
if ':' in self.env_id:
# custom environments
if '/' in self.env_id or '.' in self.env_id:
# environment in a an absolute path module written as a unix path or in a relative path module
# written as a python import path
env_class = short_dynamic_import(self.env_id)
else:
# environment in a python package
env_class = gym.envs.registration.load(self.env_id)
# instantiate the environment
try:
self.env = env_class(**self.additional_simulator_parameters)
except:
screen.error(
"Failed to instantiate Gym environment class %s with arguments %s"
% (env_class, self.additional_simulator_parameters),
crash=False)
raise
else:
self.env = gym.make(self.env_id)
# for classic control we want to use the native renderer because otherwise we will get 2 renderer windows
environment_to_always_use_with_native_rendering = [
'classic_control', 'mujoco', 'robotics'
]
self.native_rendering = self.native_rendering or \
any([env in str(self.env.unwrapped.__class__)
for env in environment_to_always_use_with_native_rendering])
if self.native_rendering:
if hasattr(self, 'renderer'):
self.renderer.close()
# seed
if self.seed is not None:
self.env.seed(self.seed)
np.random.seed(self.seed)
random.seed(self.seed)
# frame skip and max between consecutive frames
self.is_mujoco_env = 'mujoco' in str(self.env.unwrapped.__class__)
self.is_roboschool_env = 'roboschool' in str(
self.env.unwrapped.__class__)
self.is_atari_env = 'Atari' in str(self.env.unwrapped.__class__)
if self.is_atari_env:
self.env.unwrapped.frameskip = 1 # this accesses the atari env that is wrapped with a timelimit wrapper env
if self.env_id == "SpaceInvadersDeterministic-v4" and self.frame_skip == 4:
screen.warning(
"Warning: The frame-skip for Space Invaders was automatically updated from 4 to 3. "
"This is following the DQN paper where it was noticed that a frame-skip of 3 makes the "
"laser rays disappear. To force frame-skip of 4, please use SpaceInvadersNoFrameskip-v4."
)
self.frame_skip = 3
self.env = MaxOverFramesAndFrameskipEnvWrapper(
self.env,
frameskip=self.frame_skip,
max_over_num_frames=self.max_over_num_frames)
else:
self.env.unwrapped.frameskip = self.frame_skip
self.state_space = StateSpace({})
# observations
if not isinstance(self.env.observation_space, gym.spaces.dict.Dict):
state_space = {'observation': self.env.observation_space}
else:
state_space = self.env.observation_space.spaces
for observation_space_name, observation_space in state_space.items():
if observation_space_type == ObservationSpaceType.Tensor:
# we consider arbitrary input tensor which does not necessarily represent images
self.state_space[
observation_space_name] = TensorObservationSpace(
shape=np.array(observation_space.shape),
low=observation_space.low,
high=observation_space.high)
elif observation_space_type == ObservationSpaceType.Image or len(
observation_space.shape) == 3:
# we assume gym has image observations (with arbitrary number of channels) where their values are
# within 0-255, and where the channel dimension is the last dimension
if observation_space.shape[-1] in [1, 3]:
self.state_space[
observation_space_name] = ImageObservationSpace(
shape=np.array(observation_space.shape),
high=255,
channels_axis=-1)
else:
# For any number of channels other than 1 or 3, use the generic PlanarMaps space
self.state_space[
observation_space_name] = PlanarMapsObservationSpace(
shape=np.array(observation_space.shape),
low=0,
high=255,
channels_axis=-1)
elif observation_space_type == ObservationSpaceType.Vector or len(
observation_space.shape) == 1:
self.state_space[
observation_space_name] = VectorObservationSpace(
shape=observation_space.shape[0],
low=observation_space.low,
high=observation_space.high)
else:
raise screen.error(
"Failed to instantiate Gym environment class %s with observation space type %s"
% (env_class, observation_space_type),
crash=True)
if 'desired_goal' in state_space.keys():
self.goal_space = self.state_space['desired_goal']
# actions
if type(self.env.action_space) == gym.spaces.box.Box:
self.action_space = BoxActionSpace(
shape=self.env.action_space.shape,
low=self.env.action_space.low,
high=self.env.action_space.high)
elif type(self.env.action_space) == gym.spaces.discrete.Discrete:
actions_description = []
if hasattr(self.env.unwrapped, 'get_action_meanings'):
actions_description = self.env.unwrapped.get_action_meanings()
self.action_space = DiscreteActionSpace(
num_actions=self.env.action_space.n,
descriptions=actions_description)
else:
raise screen.error((
"Failed to instantiate gym environment class {} due to unsupported "
"action space {}. Expected BoxActionSpace or DiscreteActionSpace."
).format(env_class, self.env.action_space),
crash=True)
if self.human_control:
# TODO: add this to the action space
# map keyboard keys to actions
self.key_to_action = {}
if hasattr(self.env.unwrapped, 'get_keys_to_action'):
self.key_to_action = self.env.unwrapped.get_keys_to_action()
else:
screen.error(
"Error: Environment {} does not support human control.".
format(self.env),
crash=True)
# initialize the state by getting a new state from the environment
self.reset_internal_state(True)
# render
if self.is_rendered:
image = self.get_rendered_image()
scale = 1
if self.human_control:
scale = 2
if not self.native_rendering:
self.renderer.create_screen(image.shape[1] * scale,
image.shape[0] * scale)
# the info is only updated after the first step
self.state = self.step(self.action_space.default_action).next_state
self.state_space['measurements'] = VectorObservationSpace(
shape=len(self.info.keys()))
if self.env.spec and custom_reward_threshold is None:
self.reward_success_threshold = self.env.spec.reward_threshold
self.reward_space = RewardSpace(
1, reward_success_threshold=self.reward_success_threshold)
self.target_success_rate = target_success_rate
def __init__(
self,
level: LevelSelection,
frame_skip: int,
visualization_parameters: VisualizationParameters,
seed: Union[None, int] = None,
human_control: bool = False,
observation_type: ObservationType = ObservationType.Measurements,
custom_reward_threshold: Union[int, float] = None,
**kwargs):
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters)
self.observation_type = observation_type
# load and initialize environment
domain_name, task_name = self.env_id.split(":")
self.env = suite.load(domain_name=domain_name,
task_name=task_name,
task_kwargs={'random': seed})
if observation_type != ObservationType.Measurements:
self.env = pixels.Wrapper(
self.env,
pixels_only=observation_type == ObservationType.Image)
# seed
if self.seed is not None:
np.random.seed(self.seed)
random.seed(self.seed)
self.state_space = StateSpace({})
# image observations
if observation_type != ObservationType.Measurements:
self.state_space['pixels'] = ImageObservationSpace(
shape=self.env.observation_spec()['pixels'].shape, high=255)
# measurements observations
if observation_type != ObservationType.Image:
measurements_space_size = 0
measurements_names = []
for observation_space_name, observation_space in self.env.observation_spec(
).items():
if len(observation_space.shape) == 0:
measurements_space_size += 1
measurements_names.append(observation_space_name)
elif len(observation_space.shape) == 1:
measurements_space_size += observation_space.shape[0]
measurements_names.extend([
"{}_{}".format(observation_space_name, i)
for i in range(observation_space.shape[0])
])
self.state_space['measurements'] = VectorObservationSpace(
shape=measurements_space_size,
measurements_names=measurements_names)
# actions
self.action_space = BoxActionSpace(
shape=self.env.action_spec().shape[0],
low=self.env.action_spec().minimum,
high=self.env.action_spec().maximum)
# initialize the state by getting a new state from the environment
self.reset_internal_state(True)
# render
if self.is_rendered:
image = self.get_rendered_image()
scale = 1
if self.human_control:
scale = 2
if not self.native_rendering:
self.renderer.create_screen(image.shape[1] * scale,
image.shape[0] * scale)
def __init__(self, level: LevelSelection, seed: int, frame_skip: int,
human_control: bool, custom_reward_threshold: Union[int,
float],
visualization_parameters: VisualizationParameters,
server_height: int, server_width: int, camera_height: int,
camera_width: int, verbose: bool,
experiment_suite: ExperimentSuite, config: str,
episode_max_time: int, allow_braking: bool,
quality: CarlaEnvironmentParameters.Quality,
cameras: List[CameraTypes], weather_id: List[int],
experiment_path: str,
separate_actions_for_throttle_and_brake: bool,
num_speedup_steps: int, max_speed: float, **kwargs):
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters)
# server configuration
self.server_height = server_height
self.server_width = server_width
self.port = get_open_port()
self.host = 'localhost'
self.map_name = CarlaLevel[level.upper()].value['map_name']
self.map_path = CarlaLevel[level.upper()].value['map_path']
self.experiment_path = experiment_path
# client configuration
self.verbose = verbose
self.quality = quality
self.cameras = cameras
self.weather_id = weather_id
self.episode_max_time = episode_max_time
self.allow_braking = allow_braking
self.separate_actions_for_throttle_and_brake = separate_actions_for_throttle_and_brake
self.camera_width = camera_width
self.camera_height = camera_height
# setup server settings
self.experiment_suite = experiment_suite
self.config = config
if self.config:
# load settings from file
with open(self.config, 'r') as fp:
self.settings = fp.read()
else:
# hard coded settings
self.settings = CarlaSettings()
self.settings.set(SynchronousMode=True,
SendNonPlayerAgentsInfo=False,
NumberOfVehicles=15,
NumberOfPedestrians=30,
WeatherId=random.choice(
force_list(self.weather_id)),
QualityLevel=self.quality.value,
SeedVehicles=seed,
SeedPedestrians=seed)
if seed is None:
self.settings.randomize_seeds()
self.settings = self._add_cameras(self.settings, self.cameras,
self.camera_width,
self.camera_height)
# open the server
self.server = self._open_server()
logging.disable(40)
# open the client
self.game = CarlaClient(self.host, self.port, timeout=99999999)
self.game.connect()
if self.experiment_suite:
self.current_experiment_idx = 0
self.current_experiment = self.experiment_suite.get_experiments()[
self.current_experiment_idx]
self.scene = self.game.load_settings(
self.current_experiment.conditions)
else:
self.scene = self.game.load_settings(self.settings)
# get available start positions
self.positions = self.scene.player_start_spots
self.num_positions = len(self.positions)
self.current_start_position_idx = 0
self.current_pose = 0
# state space
self.state_space = StateSpace({
"measurements":
VectorObservationSpace(
4, measurements_names=["forward_speed", "x", "y", "z"])
})
for camera in self.scene.sensors:
self.state_space[camera.name] = ImageObservationSpace(
shape=np.array([self.camera_height, self.camera_width, 3]),
high=255)
# action space
if self.separate_actions_for_throttle_and_brake:
self.action_space = BoxActionSpace(
shape=3,
low=np.array([-1, 0, 0]),
high=np.array([1, 1, 1]),
descriptions=["steer", "gas", "brake"])
else:
self.action_space = BoxActionSpace(
shape=2,
low=np.array([-1, -1]),
high=np.array([1, 1]),
descriptions=["steer", "gas_and_brake"])
# human control
if self.human_control:
# convert continuous action space to discrete
self.steering_strength = 0.5
self.gas_strength = 1.0
self.brake_strength = 0.5
# TODO: reverse order of actions
self.action_space = PartialDiscreteActionSpaceMap(
target_actions=[[0., 0.], [0., -self.steering_strength],
[0., self.steering_strength],
[self.gas_strength, 0.],
[-self.brake_strength, 0],
[self.gas_strength, -self.steering_strength],
[self.gas_strength, self.steering_strength],
[self.brake_strength, -self.steering_strength],
[self.brake_strength, self.steering_strength]],
descriptions=[
'NO-OP', 'TURN_LEFT', 'TURN_RIGHT', 'GAS', 'BRAKE',
'GAS_AND_TURN_LEFT', 'GAS_AND_TURN_RIGHT',
'BRAKE_AND_TURN_LEFT', 'BRAKE_AND_TURN_RIGHT'
])
# map keyboard keys to actions
for idx, action in enumerate(self.action_space.descriptions):
for key in key_map.keys():
if action == key:
self.key_to_action[key_map[key]] = idx
self.num_speedup_steps = num_speedup_steps
self.max_speed = max_speed
# measurements
self.autopilot = None
self.planner = Planner(self.map_name)
# env initialization
self.reset_internal_state(True)
# render
if self.is_rendered:
image = self.get_rendered_image()
self.renderer.create_screen(image.shape[1], image.shape[0])
def __init__(self,
level: LevelSelection,
frame_skip: int,
visualization_parameters: VisualizationParameters,
seed: Union[None, int] = None,
human_control: bool = False,
custom_reward_threshold: Union[int, float] = None,
width: int = 84,
height: int = 84,
num_rotations: int = 24,
episode_length: int = 50000,
**kwargs):
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters)
# seed
if self.seed is not None:
np.random.seed(self.seed)
random.seed(self.seed)
self.object = Object2D(width, height, num_rotations)
self.last_result = self.object.reset()
self.state_space = StateSpace({})
# image observations
self.state_space['observation'] = PlanarMapsObservationSpace(
shape=np.array([width, height, 3]), low=0, high=255)
# measurements observations
measurements_space_size = 4
measurements_names = [
'x-position', 'y-position', 'rotation', 'num_covered_states'
]
self.state_space['measurements'] = VectorObservationSpace(
shape=measurements_space_size,
measurements_names=measurements_names)
# actions
self.num_actions = 6
self.action_space = DiscreteActionSpace(self.num_actions)
self.steps = 0
self.episode_length = episode_length
self.width = width
self.height = height
self.num_rotations = num_rotations
self.bin_size = 1
self.covered_states = np.zeros(
(int(self.width / self.bin_size), int(self.height / self.bin_size),
self.num_rotations))
self.num_covered_states = 0
# render
if self.is_rendered:
image = np.squeeze(self.object.render())
self.renderer.create_screen(image.shape[1], image.shape[0])
# initialize the state by getting a new state from the environment
self.reset_internal_state(True)
# NN configuration
agent_params.network_wrappers['main'].learning_rate = 0.0001
agent_params.network_wrappers['main'].replace_mse_with_huber_loss = False
agent_params.network_wrappers['main'].softmax_temperature = 0.2
# ER size
agent_params.memory = EpisodicExperienceReplayParameters()
DATATSET_PATH = os.path.join(os.path.expanduser('~'),
'share/Data/MLA/L2P/L2_data_rnd_RL.csv')
agent_params.memory.load_memory_from_file_path = CsvDataset(
DATATSET_PATH, True)
# E-Greedy schedule
agent_params.exploration.epsilon_schedule = LinearSchedule(0, 0, 10000)
agent_params.exploration.evaluation_epsilon = 0
spaces = SpacesDefinition(state=StateSpace(
{'observation': VectorObservationSpace(shape=7)}),
goal=None,
action=DiscreteActionSpace(4),
reward=RewardSpace(1))
graph_manager = BatchRLGraphManager(
agent_params=agent_params,
env_params=None,
spaces_definition=spaces,
schedule_params=schedule_params,
vis_params=VisualizationParameters(dump_signals_to_csv_every_x_episodes=1),
reward_model_num_epochs=30,
train_to_eval_ratio=0.4)
def __init__(self,
level: LevelSelection,
frame_skip: int,
visualization_parameters: VisualizationParameters,
target_success_rate: float = 1.0,
seed: Union[None, int] = None,
human_control: bool = False,
custom_reward_threshold: Union[int, float] = None,
screen_size: int = 84,
minimap_size: int = 64,
feature_minimap_maps_to_use: List = range(7),
feature_screen_maps_to_use: List = range(17),
observation_type:
StarcraftObservationType = StarcraftObservationType.Features,
disable_fog: bool = False,
auto_select_all_army: bool = True,
use_full_action_space: bool = False,
**kwargs):
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters,
target_success_rate)
self.screen_size = screen_size
self.minimap_size = minimap_size
self.feature_minimap_maps_to_use = feature_minimap_maps_to_use
self.feature_screen_maps_to_use = feature_screen_maps_to_use
self.observation_type = observation_type
self.features_screen_size = None
self.feature_minimap_size = None
self.rgb_screen_size = None
self.rgb_minimap_size = None
if self.observation_type == StarcraftObservationType.Features:
self.features_screen_size = screen_size
self.feature_minimap_size = minimap_size
elif self.observation_type == StarcraftObservationType.RGB:
self.rgb_screen_size = screen_size
self.rgb_minimap_size = minimap_size
self.disable_fog = disable_fog
self.auto_select_all_army = auto_select_all_army
self.use_full_action_space = use_full_action_space
# step_mul is the equivalent to frame skipping. Not sure if it repeats actions in between or not though.
self.env = sc2_env.SC2Env(
map_name=self.env_id,
step_mul=frame_skip,
visualize=self.is_rendered,
agent_interface_format=sc2_env.AgentInterfaceFormat(
feature_dimensions=sc2_env.Dimensions(
screen=self.features_screen_size,
minimap=self.feature_minimap_size)
# rgb_dimensions=sc2_env.Dimensions(
# screen=self.rgb_screen_size,
# minimap=self.rgb_screen_size
# )
),
# feature_screen_size=self.features_screen_size,
# feature_minimap_size=self.feature_minimap_size,
# rgb_screen_size=self.rgb_screen_size,
# rgb_minimap_size=self.rgb_screen_size,
disable_fog=disable_fog,
random_seed=self.seed)
# print all the available actions
# self.env = available_actions_printer.AvailableActionsPrinter(self.env)
self.reset_internal_state(True)
"""
feature_screen: [height_map, visibility_map, creep, power, player_id, player_relative, unit_type, selected,
unit_hit_points, unit_hit_points_ratio, unit_energy, unit_energy_ratio, unit_shields,
unit_shields_ratio, unit_density, unit_density_aa, effects]
feature_minimap: [height_map, visibility_map, creep, camera, player_id, player_relative, selecte
d]
player: [player_id, minerals, vespene, food_cap, food_army, food_workers, idle_worker_dount,
army_count, warp_gate_count, larva_count]
"""
self.screen_shape = np.array(
self.env.observation_spec()[0]['feature_screen'])
self.screen_shape[0] = len(self.feature_screen_maps_to_use)
self.minimap_shape = np.array(
self.env.observation_spec()[0]['feature_minimap'])
self.minimap_shape[0] = len(self.feature_minimap_maps_to_use)
self.state_space = StateSpace({
"screen":
PlanarMapsObservationSpace(shape=self.screen_shape,
low=0,
high=255,
channels_axis=0),
"minimap":
PlanarMapsObservationSpace(shape=self.minimap_shape,
low=0,
high=255,
channels_axis=0),
"measurements":
VectorObservationSpace(self.env.observation_spec()[0]["player"][0])
})
if self.use_full_action_space:
action_identifiers = list(self.env.action_spec()[0].functions)
num_action_identifiers = len(action_identifiers)
action_arguments = [(arg.name, arg.sizes)
for arg in self.env.action_spec()[0].types]
sub_action_spaces = [DiscreteActionSpace(num_action_identifiers)]
for argument in action_arguments:
for dimension in argument[1]:
sub_action_spaces.append(DiscreteActionSpace(dimension))
self.action_space = CompoundActionSpace(sub_action_spaces)
else:
self.action_space = BoxActionSpace(2,
0,
self.screen_size - 1,
["X-Axis, Y-Axis"],
default_action=np.array([
self.screen_size / 2,
self.screen_size / 2
]))
self.target_success_rate = target_success_rate
def __init__(self, level: LevelSelection, seed: int, frame_skip: int,
human_control: bool, custom_reward_threshold: Union[int,
float],
visualization_parameters: VisualizationParameters,
cameras: List[CameraTypes], **kwargs):
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters)
self.cameras = cameras
# load the emulator with the required level
self.level = DoomLevel[level.upper()]
local_scenarios_path = path.join(
os.path.dirname(os.path.realpath(__file__)), 'doom')
self.scenarios_dir = local_scenarios_path if 'COACH_LOCAL' in level \
else path.join(environ.get('VIZDOOM_ROOT'), 'scenarios')
self.game = vizdoom.DoomGame()
self.game.load_config(path.join(self.scenarios_dir, self.level.value))
self.game.set_window_visible(False)
self.game.add_game_args("+vid_forcesurface 1")
self.wait_for_explicit_human_action = True
if self.human_control:
self.game.set_screen_resolution(
vizdoom.ScreenResolution.RES_640X480)
elif self.is_rendered:
self.game.set_screen_resolution(
vizdoom.ScreenResolution.RES_320X240)
else:
# lower resolution since we actually take only 76x60 and we don't need to render
self.game.set_screen_resolution(
vizdoom.ScreenResolution.RES_160X120)
self.game.set_render_hud(False)
self.game.set_render_crosshair(False)
self.game.set_render_decals(False)
self.game.set_render_particles(False)
for camera in self.cameras:
if hasattr(self.game, 'set_{}_enabled'.format(camera.value[1])):
getattr(self.game,
'set_{}_enabled'.format(camera.value[1]))(True)
self.game.init()
# actions
actions_description = ['NO-OP']
actions_description += [
str(action).split(".")[1]
for action in self.game.get_available_buttons()
]
actions_description = actions_description[::-1]
self.action_space = MultiSelectActionSpace(
self.game.get_available_buttons_size(),
max_simultaneous_selected_actions=1,
descriptions=actions_description,
allow_no_action_to_be_selected=True)
# human control
if self.human_control:
# TODO: add this to the action space
# map keyboard keys to actions
for idx, action in enumerate(self.action_space.descriptions):
if action in key_map.keys():
self.key_to_action[(key_map[action], )] = idx
# states
self.state_space = StateSpace({
"measurements":
VectorObservationSpace(
self.game.get_state().game_variables.shape[0],
measurements_names=[
str(m) for m in self.game.get_available_game_variables()
])
})
for camera in self.cameras:
self.state_space[camera.value[0]] = ImageObservationSpace(
shape=np.array([
self.game.get_screen_height(),
self.game.get_screen_width(), 3
]),
high=255)
# seed
if seed is not None:
self.game.set_seed(seed)
self.reset_internal_state()
# render
if self.is_rendered:
image = self.get_rendered_image()
self.renderer.create_screen(image.shape[1], image.shape[0])
def __init__(self,
level: LevelSelection,
frame_skip: int,
visualization_parameters: VisualizationParameters,
additional_simulator_parameters: Dict[str, Any] = None,
seed: Union[None, int] = None,
human_control: bool = False,
custom_reward_threshold: Union[int, float] = None,
random_initialization_steps: int = 1,
max_over_num_frames: int = 1,
**kwargs):
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters)
self.random_initialization_steps = random_initialization_steps
self.max_over_num_frames = max_over_num_frames
self.additional_simulator_parameters = additional_simulator_parameters
# hide warnings
gym.logger.set_level(40)
"""
load and initialize environment
environment ids can be defined in 3 ways:
1. Native gym environments like BreakoutDeterministic-v0 for example
2. Custom gym environments written and installed as python packages.
This environments should have a python module with a class inheriting gym.Env, implementing the
relevant functions (_reset, _step, _render) and defining the observation and action space
For example: my_environment_package:MyEnvironmentClass will run an environment defined in the
MyEnvironmentClass class
3. Custom gym environments written as an independent module which is not installed.
This environments should have a python module with a class inheriting gym.Env, implementing the
relevant functions (_reset, _step, _render) and defining the observation and action space.
For example: path_to_my_environment.sub_directory.my_module:MyEnvironmentClass will run an
environment defined in the MyEnvironmentClass class which is located in the module in the relative path
path_to_my_environment.sub_directory.my_module
"""
if ':' in self.env_id:
# custom environments
if '/' in self.env_id or '.' in self.env_id:
# environment in a an absolute path module written as a unix path or in a relative path module
# written as a python import path
env_class = short_dynamic_import(self.env_id)
else:
# environment in a python package
env_class = gym.envs.registration.load(self.env_id)
# instantiate the environment
if self.additional_simulator_parameters:
self.env = env_class(**self.additional_simulator_parameters)
else:
self.env = env_class()
else:
self.env = gym.make(self.env_id)
# for classic control we want to use the native renderer because otherwise we will get 2 renderer windows
environment_to_always_use_with_native_rendering = [
'classic_control', 'mujoco', 'robotics'
]
self.native_rendering = self.native_rendering or \
any([env in str(self.env.unwrapped.__class__)
for env in environment_to_always_use_with_native_rendering])
if self.native_rendering:
if hasattr(self, 'renderer'):
self.renderer.close()
# seed
if self.seed is not None:
self.env.seed(self.seed)
np.random.seed(self.seed)
random.seed(self.seed)
# frame skip and max between consecutive frames
self.is_robotics_env = 'robotics' in str(self.env.unwrapped.__class__)
self.is_mujoco_env = 'mujoco' in str(self.env.unwrapped.__class__)
self.is_atari_env = 'Atari' in str(self.env.unwrapped.__class__)
self.timelimit_env_wrapper = self.env
if self.is_atari_env:
self.env.unwrapped.frameskip = 1 # this accesses the atari env that is wrapped with a timelimit wrapper env
if self.env_id == "SpaceInvadersDeterministic-v4" and self.frame_skip == 4:
screen.warning(
"Warning: The frame-skip for Space Invaders was automatically updated from 4 to 3. "
"This is following the DQN paper where it was noticed that a frame-skip of 3 makes the "
"laser rays disappear. To force frame-skip of 4, please use SpaceInvadersNoFrameskip-v4."
)
self.frame_skip = 3
self.env = MaxOverFramesAndFrameskipEnvWrapper(
self.env,
frameskip=self.frame_skip,
max_over_num_frames=self.max_over_num_frames)
else:
self.env.unwrapped.frameskip = self.frame_skip
self.state_space = StateSpace({})
# observations
if not isinstance(self.env.observation_space,
gym.spaces.dict_space.Dict):
state_space = {'observation': self.env.observation_space}
else:
state_space = self.env.observation_space.spaces
for observation_space_name, observation_space in state_space.items():
if len(observation_space.shape
) == 3 and observation_space.shape[-1] == 3:
# we assume gym has image observations which are RGB and where their values are within 0-255
self.state_space[
observation_space_name] = ImageObservationSpace(
shape=np.array(observation_space.shape),
high=255,
channels_axis=-1)
else:
self.state_space[
observation_space_name] = VectorObservationSpace(
shape=observation_space.shape[0],
low=observation_space.low,
high=observation_space.high)
if 'desired_goal' in state_space.keys():
self.goal_space = self.state_space['desired_goal']
# actions
if type(self.env.action_space) == gym.spaces.box.Box:
self.action_space = BoxActionSpace(
shape=self.env.action_space.shape,
low=self.env.action_space.low,
high=self.env.action_space.high)
elif type(self.env.action_space) == gym.spaces.discrete.Discrete:
actions_description = []
if hasattr(self.env.unwrapped, 'get_action_meanings'):
actions_description = self.env.unwrapped.get_action_meanings()
self.action_space = DiscreteActionSpace(
num_actions=self.env.action_space.n,
descriptions=actions_description)
if self.human_control:
# TODO: add this to the action space
# map keyboard keys to actions
self.key_to_action = {}
if hasattr(self.env.unwrapped, 'get_keys_to_action'):
self.key_to_action = self.env.unwrapped.get_keys_to_action()
# initialize the state by getting a new state from the environment
self.reset_internal_state(True)
# render
if self.is_rendered:
image = self.get_rendered_image()
scale = 1
if self.human_control:
scale = 2
if not self.native_rendering:
self.renderer.create_screen(image.shape[1] * scale,
image.shape[0] * scale)
# measurements
if self.env.spec is not None:
self.timestep_limit = self.env.spec.timestep_limit
else:
self.timestep_limit = None
# the info is only updated after the first step
self.state = self.step(self.action_space.default_action).next_state
self.state_space['measurements'] = VectorObservationSpace(
shape=len(self.info.keys()))
if self.env.spec and custom_reward_threshold is None:
self.reward_success_threshold = self.env.spec.reward_threshold
self.reward_space = RewardSpace(
1, reward_success_threshold=self.reward_success_threshold)
def __init__(
self,
level: LevelSelection,
frame_skip: int,
visualization_parameters: VisualizationParameters,
target_success_rate: float = 1.0,
seed: Union[None, int] = None,
human_control: bool = False,
observation_type: ObservationType = ObservationType.Measurements,
custom_reward_threshold: Union[int, float] = None,
**kwargs):
"""
:param level: (str)
A string representing the control suite level to run. This can also be a LevelSelection object.
For example, cartpole:swingup.
:param frame_skip: (int)
The number of frames to skip between any two actions given by the agent. The action will be repeated
for all the skipped frames.
:param visualization_parameters: (VisualizationParameters)
The parameters used for visualizing the environment, such as the render flag, storing videos etc.
:param target_success_rate: (float)
Stop experiment if given target success rate was achieved.
:param seed: (int)
A seed to use for the random number generator when running the environment.
:param human_control: (bool)
A flag that allows controlling the environment using the keyboard keys.
:param observation_type: (ObservationType)
An enum which defines which observation to use. The current options are to use:
* Measurements only - a vector of joint torques and similar measurements
* Image only - an image of the environment as seen by a camera attached to the simulator
* Measurements & Image - both type of observations will be returned in the state using the keys
'measurements' and 'pixels' respectively.
:param custom_reward_threshold: (float)
Allows defining a custom reward that will be used to decide when the agent succeeded in passing the environment.
"""
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters,
target_success_rate)
self.observation_type = observation_type
# load and initialize environment
domain_name, task_name = self.env_id.split(":")
self.env = suite.load(domain_name=domain_name,
task_name=task_name,
task_kwargs={'random': seed})
if observation_type != ObservationType.Measurements:
self.env = pixels.Wrapper(
self.env,
pixels_only=observation_type == ObservationType.Image)
# seed
if self.seed is not None:
np.random.seed(self.seed)
random.seed(self.seed)
self.state_space = StateSpace({})
# image observations
if observation_type != ObservationType.Measurements:
self.state_space['pixels'] = ImageObservationSpace(
shape=self.env.observation_spec()['pixels'].shape, high=255)
# measurements observations
if observation_type != ObservationType.Image:
measurements_space_size = 0
measurements_names = []
for observation_space_name, observation_space in self.env.observation_spec(
).items():
if len(observation_space.shape) == 0:
measurements_space_size += 1
measurements_names.append(observation_space_name)
elif len(observation_space.shape) == 1:
measurements_space_size += observation_space.shape[0]
measurements_names.extend([
"{}_{}".format(observation_space_name, i)
for i in range(observation_space.shape[0])
])
self.state_space['measurements'] = VectorObservationSpace(
shape=measurements_space_size,
measurements_names=measurements_names)
# actions
self.action_space = BoxActionSpace(
shape=self.env.action_spec().shape[0],
low=self.env.action_spec().minimum,
high=self.env.action_spec().maximum)
# initialize the state by getting a new state from the environment
self.reset_internal_state(True)
# render
if self.is_rendered:
image = self.get_rendered_image()
scale = 1
if self.human_control:
scale = 2
if not self.native_rendering:
self.renderer.create_screen(image.shape[1] * scale,
image.shape[0] * scale)
self.target_success_rate = target_success_rate
def __init__(self,
level: LevelSelection,
seed: int,
frame_skip: int,
human_control: bool,
custom_reward_threshold: Union[int, float],
visualization_parameters: VisualizationParameters,
cameras: List[CameraTypes],
target_success_rate: float = 1.0,
**kwargs):
"""
:param level: (str)
A string representing the doom level to run. This can also be a LevelSelection object.
This should be one of the levels defined in the DoomLevel enum. For example, HEALTH_GATHERING.
:param seed: (int)
A seed to use for the random number generator when running the environment.
:param frame_skip: (int)
The number of frames to skip between any two actions given by the agent. The action will be repeated
for all the skipped frames.
:param human_control: (bool)
A flag that allows controlling the environment using the keyboard keys.
:param custom_reward_threshold: (float)
Allows defining a custom reward that will be used to decide when the agent succeeded in passing the environment.
:param visualization_parameters: (VisualizationParameters)
The parameters used for visualizing the environment, such as the render flag, storing videos etc.
:param cameras: (List[CameraTypes])
A list of camera types to use as observation in the state returned from the environment.
Each camera should be an enum from CameraTypes, and there are several options like an RGB observation,
a depth map, a segmentation map, and a top down map of the enviornment.
:param target_success_rate: (float)
Stop experiment if given target success rate was achieved.
"""
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters,
target_success_rate)
self.cameras = cameras
# load the emulator with the required level
self.level = DoomLevel[level.upper()]
local_scenarios_path = path.join(
os.path.dirname(os.path.realpath(__file__)), 'doom')
if 'COACH_LOCAL' in level:
self.scenarios_dir = local_scenarios_path
elif 'VIZDOOM_ROOT' in environ:
self.scenarios_dir = path.join(environ.get('VIZDOOM_ROOT'),
'scenarios')
else:
self.scenarios_dir = path.join(
os.path.dirname(os.path.realpath(vizdoom.__file__)),
'scenarios')
self.game = vizdoom.DoomGame()
self.game.load_config(path.join(self.scenarios_dir, self.level.value))
self.game.set_window_visible(False)
self.game.add_game_args("+vid_forcesurface 1")
self.wait_for_explicit_human_action = True
if self.human_control:
self.game.set_screen_resolution(
vizdoom.ScreenResolution.RES_640X480)
elif self.is_rendered:
self.game.set_screen_resolution(
vizdoom.ScreenResolution.RES_320X240)
else:
# lower resolution since we actually take only 76x60 and we don't need to render
self.game.set_screen_resolution(
vizdoom.ScreenResolution.RES_160X120)
self.game.set_render_hud(False)
self.game.set_render_crosshair(False)
self.game.set_render_decals(False)
self.game.set_render_particles(False)
for camera in self.cameras:
if hasattr(self.game, 'set_{}_enabled'.format(camera.value[1])):
getattr(self.game,
'set_{}_enabled'.format(camera.value[1]))(True)
self.game.init()
# actions
actions_description = ['NO-OP']
actions_description += [
str(action).split(".")[1]
for action in self.game.get_available_buttons()
]
actions_description = actions_description[::-1]
self.action_space = MultiSelectActionSpace(
self.game.get_available_buttons_size(),
max_simultaneous_selected_actions=1,
descriptions=actions_description,
allow_no_action_to_be_selected=True)
# human control
if self.human_control:
# TODO: add this to the action space
# map keyboard keys to actions
for idx, action in enumerate(self.action_space.descriptions):
if action in key_map.keys():
self.key_to_action[(key_map[action], )] = idx
# states
self.state_space = StateSpace({
"measurements":
VectorObservationSpace(
self.game.get_state().game_variables.shape[0],
measurements_names=[
str(m) for m in self.game.get_available_game_variables()
])
})
for camera in self.cameras:
self.state_space[camera.value[0]] = ImageObservationSpace(
shape=np.array([
self.game.get_screen_height(),
self.game.get_screen_width(), 3
]),
high=255)
# seed
if seed is not None:
self.game.set_seed(seed)
self.reset_internal_state()
# render
if self.is_rendered:
image = self.get_rendered_image()
self.renderer.create_screen(image.shape[1], image.shape[0])
self.target_success_rate = target_success_rate
