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mirror of https://github.com/gryf/coach.git synced 2025-12-17 11:10:20 +01:00

Release 0.9

Main changes are detailed below:

New features -
* CARLA 0.7 simulator integration
* Human control of the game play
* Recording of human game play and storing / loading the replay buffer
* Behavioral cloning agent and presets
* Golden tests for several presets
* Selecting between deep / shallow image embedders
* Rendering through pygame (with some boost in performance)

API changes -
* Improved environment wrapper API
* Added an evaluate flag to allow convenient evaluation of existing checkpoints
* Improve frameskip definition in Gym

Bug fixes -
* Fixed loading of checkpoints for agents with more than one network
* Fixed the N Step Q learning agent python3 compatibility
This commit is contained in:
Itai Caspi
2017-12-19 19:27:16 +02:00
committed by GitHub
parent 11faf19649
commit 125c7ee38d
41 changed files with 1713 additions and 260 deletions

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import sys
from os import path, environ
try:
sys.path.append(path.join(environ.get('CARLA_ROOT'), 'PythonClient'))
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
except ImportError:
from logger import failed_imports
failed_imports.append("CARLA")
import numpy as np
import time
import logging
import subprocess
import signal
from environments.environment_wrapper import EnvironmentWrapper
from utils import *
from logger import screen, logger
from PIL import Image
# enum of the available levels and their path
class CarlaLevel(Enum):
TOWN1 = "/Game/Maps/Town01"
TOWN2 = "/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),
}
class CarlaEnvironmentWrapper(EnvironmentWrapper):
def __init__(self, tuning_parameters):
EnvironmentWrapper.__init__(self, tuning_parameters)
self.tp = tuning_parameters
# server configuration
self.server_height = self.tp.env.server_height
self.server_width = self.tp.env.server_width
self.port = get_open_port()
self.host = 'localhost'
self.map = CarlaLevel().get(self.tp.env.level)
# client configuration
self.verbose = self.tp.env.verbose
self.depth = self.tp.env.depth
self.stereo = self.tp.env.stereo
self.semantic_segmentation = self.tp.env.semantic_segmentation
self.height = self.server_height * (1 + int(self.depth) + int(self.semantic_segmentation))
self.width = self.server_width * (1 + int(self.stereo))
self.size = (self.width, self.height)
self.config = self.tp.env.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=1)
self.settings.randomize_seeds()
# add cameras
camera = Camera('CameraRGB')
camera.set_image_size(self.width, self.height)
camera.set_position(200, 0, 140)
camera.set_rotation(0, 0, 0)
self.settings.add_sensor(camera)
# 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()
scene = self.game.load_settings(self.settings)
# get available start positions
positions = scene.player_start_spots
self.num_pos = len(positions)
self.iterator_start_positions = 0
# action space
self.discrete_controls = False
self.action_space_size = 2
self.action_space_high = [1, 1]
self.action_space_low = [-1, -1]
self.action_space_abs_range = np.maximum(np.abs(self.action_space_low), np.abs(self.action_space_high))
self.steering_strength = 0.5
self.gas_strength = 1.0
self.brake_strength = 0.5
self.actions = {0: [0., 0.],
1: [0., -self.steering_strength],
2: [0., self.steering_strength],
3: [self.gas_strength, 0.],
4: [-self.brake_strength, 0],
5: [self.gas_strength, -self.steering_strength],
6: [self.gas_strength, self.steering_strength],
7: [self.brake_strength, -self.steering_strength],
8: [self.brake_strength, self.steering_strength]}
self.actions_description = ['NO-OP', 'TURN_LEFT', 'TURN_RIGHT', 'GAS', 'BRAKE',
'GAS_AND_TURN_LEFT', 'GAS_AND_TURN_RIGHT',
'BRAKE_AND_TURN_LEFT', 'BRAKE_AND_TURN_RIGHT']
for idx, action in enumerate(self.actions_description):
for key in key_map.keys():
if action == key:
self.key_to_action[key_map[key]] = idx
self.num_speedup_steps = 30
# measurements
self.measurements_size = (1,)
self.autopilot = None
# env initialization
self.reset(True)
# render
if self.is_rendered:
image = self.get_rendered_image()
self.renderer.create_screen(image.shape[1], image.shape[0])
def _open_server(self):
log_path = path.join(logger.experiments_path, "CARLA_LOG_{}.txt".format(self.port))
with open(log_path, "wb") as out:
cmd = [path.join(environ.get('CARLA_ROOT'), 'CarlaUE4.sh'), self.map,
"-benchmark", "-carla-server", "-fps=10", "-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.observation = sensor_data['CameraRGB'].data
self.location = (measurements.player_measurements.transform.location.x,
measurements.player_measurements.transform.location.y,
measurements.player_measurements.transform.location.z)
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.autopilot = measurements.player_measurements.autopilot_control
# action_p = ['%.2f' % member for member in [self.control.throttle, self.control.steer]]
# screen.success('REWARD: %.2f, ACTIONS: %s' % (self.reward, action_p))
if (measurements.game_timestamp >= self.tp.env.episode_max_time) or is_collision:
# screen.success('EPISODE IS DONE. GameTime: {}, Collision: {}'.format(str(measurements.game_timestamp),
# str(is_collision)))
self.done = True
def _take_action(self, action_idx):
if type(action_idx) == int:
action = self.actions[action_idx]
else:
action = action_idx
self.last_action_idx = action
self.control = VehicleControl()
self.control.throttle = np.clip(action[0], 0, 1)
self.control.steer = np.clip(action[1], -1, 1)
self.control.brake = np.abs(np.clip(action[0], -1, 0))
if not self.tp.env.allow_braking:
self.control.brake = 0
self.control.hand_brake = False
self.control.reverse = False
self.game.send_control(self.control)
def _restart_environment_episode(self, force_environment_reset=False):
self.iterator_start_positions += 1
if self.iterator_start_positions >= self.num_pos:
self.iterator_start_positions = 0
try:
self.game.start_episode(self.iterator_start_positions)
except:
self.game.connect()
self.game.start_episode(self.iterator_start_positions)
# start the game with some initial speed
observation = None
for i in range(self.num_speedup_steps):
observation = self.step([1.0, 0])['observation']
self.observation = observation
return observation