#
# Copyright (c) 2017 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import random
import sys
from os import path, environ
from rl_coach.logger import screen
from rl_coach.filters.action.partial_discrete_action_space_map import PartialDiscreteActionSpaceMap
from rl_coach.filters.observation.observation_rgb_to_y_filter import ObservationRGBToYFilter
from rl_coach.filters.observation.observation_to_uint8_filter import ObservationToUInt8Filter
try:
if 'CARLA_ROOT' in environ:
sys.path.append(path.join(environ.get('CARLA_ROOT'), 'PythonClient'))
else:
screen.error("CARLA_ROOT was not defined. Please set it to point to the CARLA root directory and try again.", crash=False)
from carla.client import CarlaClient
from carla.settings import CarlaSettings
from carla.tcp import TCPConnectionError
from carla.sensor import Camera
from carla.client import VehicleControl
from carla.planner.planner import Planner
from carla.driving_benchmark.experiment_suites.experiment_suite import ExperimentSuite
except ImportError:
from rl_coach.logger import failed_imports
failed_imports.append("CARLA")
import os
import signal
import logging
import subprocess
import numpy as np
from rl_coach.environments.environment import Environment, EnvironmentParameters, LevelSelection
from rl_coach.spaces import BoxActionSpace, ImageObservationSpace, StateSpace, VectorObservationSpace
from rl_coach.utils import get_open_port, force_list
from enum import Enum
from typing import List, Union
from rl_coach.base_parameters import VisualizationParameters
from rl_coach.filters.filter import InputFilter, NoOutputFilter
from rl_coach.filters.observation.observation_rescale_to_size_filter import ObservationRescaleToSizeFilter
from rl_coach.filters.observation.observation_stacking_filter import ObservationStackingFilter
# enum of the available levels and their path
class CarlaLevel(Enum):
TOWN1 = {"map_name": "Town01", "map_path": "/Game/Maps/Town01"}
TOWN2 = {"map_name": "Town02", "map_path": "/Game/Maps/Town02"}
key_map = {
'BRAKE': (274,), # down arrow
'GAS': (273,), # up arrow
'TURN_LEFT': (276,), # left arrow
'TURN_RIGHT': (275,), # right arrow
'GAS_AND_TURN_LEFT': (273, 276),
'GAS_AND_TURN_RIGHT': (273, 275),
'BRAKE_AND_TURN_LEFT': (274, 276),
'BRAKE_AND_TURN_RIGHT': (274, 275),
}
CarlaInputFilter = InputFilter(is_a_reference_filter=True)
CarlaInputFilter.add_observation_filter('forward_camera', 'rescaling',
ObservationRescaleToSizeFilter(ImageObservationSpace(np.array([128, 180, 3]),
high=255)))
CarlaInputFilter.add_observation_filter('forward_camera', 'to_grayscale', ObservationRGBToYFilter())
CarlaInputFilter.add_observation_filter('forward_camera', 'to_uint8', ObservationToUInt8Filter(0, 255))
CarlaInputFilter.add_observation_filter('forward_camera', 'stacking', ObservationStackingFilter(4))
CarlaOutputFilter = NoOutputFilter()
class CameraTypes(Enum):
FRONT = "forward_camera"
LEFT = "left_camera"
RIGHT = "right_camera"
SEGMENTATION = "segmentation"
DEPTH = "depth"
LIDAR = "lidar"
class CarlaEnvironmentParameters(EnvironmentParameters):
class Quality(Enum):
LOW = "Low"
EPIC = "Epic"
def __init__(self, level="town1"):
super().__init__(level=level)
self.frame_skip = 3 # the frame skip affects the fps of the server directly. fps = 30 / frameskip
self.server_height = 512
self.server_width = 720
self.camera_height = 128
self.camera_width = 180
self.experiment_suite = None # an optional CARLA experiment suite to use
self.config = None
self.level = level
self.quality = self.Quality.LOW
self.cameras = [CameraTypes.FRONT]
self.weather_id = [1]
self.verbose = True
self.episode_max_time = 100000 # miliseconds for each episode
self.allow_braking = False
self.separate_actions_for_throttle_and_brake = False
self.num_speedup_steps = 30
self.max_speed = 35.0 # km/h
self.default_input_filter = CarlaInputFilter
self.default_output_filter = CarlaOutputFilter
@property
def path(self):
return 'rl_coach.environments.carla_environment:CarlaEnvironment'
[docs]class CarlaEnvironment(Environment):
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, target_success_rate: float = 1.0, **kwargs):
super().__init__(level, seed, frame_skip, human_control, custom_reward_threshold, visualization_parameters, target_success_rate)
# 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])
self.target_success_rate = target_success_rate
def _add_cameras(self, settings, cameras, camera_width, camera_height):
# add a front facing camera
if CameraTypes.FRONT in cameras:
camera = Camera(CameraTypes.FRONT.value)
camera.set(FOV=100)
camera.set_image_size(camera_width, camera_height)
camera.set_position(2.0, 0, 1.4)
camera.set_rotation(-15.0, 0, 0)
settings.add_sensor(camera)
# add a left facing camera
if CameraTypes.LEFT in cameras:
camera = Camera(CameraTypes.LEFT.value)
camera.set(FOV=100)
camera.set_image_size(camera_width, camera_height)
camera.set_position(2.0, 0, 1.4)
camera.set_rotation(-15.0, -30, 0)
settings.add_sensor(camera)
# add a right facing camera
if CameraTypes.RIGHT in cameras:
camera = Camera(CameraTypes.RIGHT.value)
camera.set(FOV=100)
camera.set_image_size(camera_width, camera_height)
camera.set_position(2.0, 0, 1.4)
camera.set_rotation(-15.0, 30, 0)
settings.add_sensor(camera)
# add a front facing depth camera
if CameraTypes.DEPTH in cameras:
camera = Camera(CameraTypes.DEPTH.value)
camera.set_image_size(camera_width, camera_height)
camera.set_position(0.2, 0, 1.3)
camera.set_rotation(8, 30, 0)
camera.PostProcessing = 'Depth'
settings.add_sensor(camera)
# add a front facing semantic segmentation camera
if CameraTypes.SEGMENTATION in cameras:
camera = Camera(CameraTypes.SEGMENTATION.value)
camera.set_image_size(camera_width, camera_height)
camera.set_position(0.2, 0, 1.3)
camera.set_rotation(8, 30, 0)
camera.PostProcessing = 'SemanticSegmentation'
settings.add_sensor(camera)
return settings
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self.planner.get_next_command(
(current_point.location.x,
current_point.location.y, 0.22),
(current_point.orientation.x,
current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y, end_point.orientation.z))
return directions
def _open_server(self):
log_path = path.join(self.experiment_path if self.experiment_path is not None else '.', 'logs',
"CARLA_LOG_{}.txt".format(self.port))
if not os.path.exists(os.path.dirname(log_path)):
os.makedirs(os.path.dirname(log_path))
with open(log_path, "wb") as out:
cmd = [path.join(environ.get('CARLA_ROOT'), 'CarlaUE4.sh'), self.map_path,
"-benchmark", "-carla-server", "-fps={}".format(30 / self.frame_skip),
"-world-port={}".format(self.port),
"-windowed -ResX={} -ResY={}".format(self.server_width, self.server_height),
"-carla-no-hud"]
if self.config:
cmd.append("-carla-settings={}".format(self.config))
p = subprocess.Popen(cmd, stdout=out, stderr=out)
return p
def _close_server(self):
os.killpg(os.getpgid(self.server.pid), signal.SIGKILL)
def _update_state(self):
# get measurements and observations
measurements = []
while type(measurements) == list:
measurements, sensor_data = self.game.read_data()
self.state = {}
for camera in self.scene.sensors:
self.state[camera.name] = sensor_data[camera.name].data
self.location = [measurements.player_measurements.transform.location.x,
measurements.player_measurements.transform.location.y,
measurements.player_measurements.transform.location.z]
self.distance_from_goal = np.linalg.norm(np.array(self.location[:2]) -
[self.current_goal.location.x, self.current_goal.location.y])
is_collision = measurements.player_measurements.collision_vehicles != 0 \
or measurements.player_measurements.collision_pedestrians != 0 \
or measurements.player_measurements.collision_other != 0
speed_reward = measurements.player_measurements.forward_speed - 1
if speed_reward > 30.:
speed_reward = 30.
self.reward = speed_reward \
- (measurements.player_measurements.intersection_otherlane * 5) \
- (measurements.player_measurements.intersection_offroad * 5) \
- is_collision * 100 \
- np.abs(self.control.steer) * 10
# update measurements
self.measurements = [measurements.player_measurements.forward_speed] + self.location
self.autopilot = measurements.player_measurements.autopilot_control
# The directions to reach the goal (0 Follow lane, 1 Left, 2 Right, 3 Straight)
directions = int(self._get_directions(measurements.player_measurements.transform, self.current_goal) - 2)
self.state['high_level_command'] = directions
if (measurements.game_timestamp >= self.episode_max_time) or is_collision:
self.done = True
self.state['measurements'] = np.array(self.measurements)
def _take_action(self, action):
self.control = VehicleControl()
if self.separate_actions_for_throttle_and_brake:
self.control.steer = np.clip(action[0], -1, 1)
self.control.throttle = np.clip(action[1], 0, 1)
self.control.brake = np.clip(action[2], 0, 1)
else:
# transform the 2 value action (steer, throttle - brake) into a 3 value action (steer, throttle, brake)
self.control.steer = np.clip(action[0], -1, 1)
self.control.throttle = np.clip(action[1], 0, 1)
self.control.brake = np.abs(np.clip(action[1], -1, 0))
# prevent braking
if not self.allow_braking or self.control.brake < 0.1 or self.control.throttle > self.control.brake:
self.control.brake = 0
# prevent over speeding
if hasattr(self, 'measurements') and self.measurements[0] * 3.6 > self.max_speed and self.control.brake == 0:
self.control.throttle = 0.0
self.control.hand_brake = False
self.control.reverse = False
self.game.send_control(self.control)
def _load_experiment(self, experiment_idx):
self.current_experiment = self.experiment_suite.get_experiments()[experiment_idx]
self.scene = self.game.load_settings(self.current_experiment.conditions)
self.positions = self.scene.player_start_spots
self.num_positions = len(self.positions)
self.current_start_position_idx = 0
self.current_pose = 0
def _restart_environment_episode(self, force_environment_reset=False):
# select start and end positions
if self.experiment_suite:
# if an expeirent suite is available, follow its given poses
if self.current_pose >= len(self.current_experiment.poses):
# load a new experiment
self.current_experiment_idx = (self.current_experiment_idx + 1) % len(self.experiment_suite.get_experiments())
self._load_experiment(self.current_experiment_idx)
self.current_start_position_idx = self.current_experiment.poses[self.current_pose][0]
self.current_goal = self.positions[self.current_experiment.poses[self.current_pose][1]]
self.current_pose += 1
else:
# go over all the possible positions in a cyclic manner
self.current_start_position_idx = (self.current_start_position_idx + 1) % self.num_positions
# choose a random goal destination
self.current_goal = random.choice(self.positions)
try:
self.game.start_episode(self.current_start_position_idx)
except:
self.game.connect()
self.game.start_episode(self.current_start_position_idx)
# start the game with some initial speed
for i in range(self.num_speedup_steps):
self.control = VehicleControl(throttle=1.0, brake=0, steer=0, hand_brake=False, reverse=False)
self.game.send_control(VehicleControl())
def get_rendered_image(self) -> np.ndarray:
"""
Return a numpy array containing the image that will be rendered to the screen.
This can be different from the observation. For example, mujoco's observation is a measurements vector.
:return: numpy array containing the image that will be rendered to the screen
"""
image = [self.state[camera.name] for camera in self.scene.sensors]
image = np.vstack(image)
return image
def get_target_success_rate(self) -> float:
return self.target_success_rate