def default_config(cls) -> PGConfig:
config = PGDriveEnv.default_config()
# Set the internal environment run in 0.02s interval.
config["decision_repeat"] = 1
# Speed reward_function is given for current state, so its magnitude need to be reduced
original_action_repeat = PGDriveEnv.default_config()["decision_repeat"]
config[
"speed_reward"] = config["speed_reward"] / original_action_repeat
# Set the interval from 0.02s to 1s
config.update(
{
"fixed_action_repeat": 0,
"max_action_repeat": 50,
"min_action_repeat": 1,
"horizon": 5000,
}, )
# default gamma for ORIGINAL primitive step!
# Note that we will change this term since ORIGINAL primitive steps is not the internal step!
# It still contains ORIGINAL_ACTION_STEP internal steps!
# So we will modify this gamma to make sure it behaves like the one applied to ORIGINAL primitive steps.
# config.add("gamma", 0.99)
return config
def default_config() -> PGConfig:
config = PGDriveEnv.default_config()
config.add("change_friction", True)
config.add("friction_min", 0.8)
config.add("friction_max", 1.2)
config.update({"vehicle_config": {"wheel_friction": 1.0}})
return config
def default_config() -> PGConfig:
config = PGDriveEnv.default_config()
config.update({
"change_density": True,
"density_min": 0.0,
"density_max": 0.4,
})
return config
def default_config() -> PGConfig:
config = PGDriveEnv.default_config()
config.update({
"change_friction": True,
"friction_min": 0.8,
"friction_max": 1.2,
"vehicle_config": {
"wheel_friction": 1.0
}
})
return config
def default_config(cls) -> PGConfig:
config = PGDriveEnv.default_config()
config["vehicle_config"]["lidar"].update({"num_lasers": 0, "distance": 0}) # Remove lidar
config.update(
{
"frame_skip": 5,
"frame_stack": 3,
"post_stack": 5,
"rgb_clip": True,
"resolution_size": 84,
"distance": 30
}
)
return config
def default_config(cls) -> PGConfig:
config = PGDriveEnv.default_config()
# Set the internal environment run in 0.02s interval.
config["decision_repeat"] = 1
# Speed reward is given for current state, so its magnitude need to be reduced
config["speed_reward"] = config["speed_reward"] / cls.ORIGINAL_ACTION_REPEAT
# Set the interval from 0.02s to 1s
config.add("fixed_action_repeat", 0) # 0 stands for using varying action repeat.
config.add("max_action_repeat", 50)
config.add("min_action_repeat", 1)
config.add("horizon", 5000) # How many primitive steps within one episode
# default gamma for ORIGINAL primitive step!
# Note that we will change this term since ORIGINAL primitive steps is not the internal step!
# It still contains ORIGINAL_ACTION_STEP internal steps!
# So we will modify this gamma to make sure it behaves like the one applied to ORIGINAL primitive steps.
# config.add("gamma", 0.99)
return config
@staticmethod
def argoverse_position(pos):
pos[1] *= -1
return pos
@staticmethod
def pgdrive_position(pos):
pos[1] *= -1
return pos
if __name__ == "__main__":
from pgdrive.engine.engine_utils import initialize_engine
from pgdrive.envs.pgdrive_env import PGDriveEnv
default_config = PGDriveEnv.default_config()
default_config["use_render"] = True
default_config["debug"] = True
engine = initialize_engine(default_config)
# in argoverse coordinates
xcenter, ycenter = 2599.5505965123866, 1200.0214763629717
xmin = xcenter - 80 # 150
xmax = xcenter + 80 # 150
ymin = ycenter - 80 # 150
ymax = ycenter + 80 # 150
map = ArgoverseMap({
"city":
"PIT",
# "draw_map_resolution": 1024,
"center":
def default_config() -> Config:
config = PGDriveEnv.default_config()
config.update(MULTI_AGENT_PGDRIVE_DEFAULT_CONFIG)
return config
def default_config() -> PGConfig:
config = PGDriveEnv.default_config()
config.add("change_density", True)
config.add("density_min", 0.0)
config.add("density_max", 0.4)
return config
def default_config() -> PGConfig:
config = PGDriveEnvV1.default_config()
config.update(
dict(
# ===== Traffic =====
traffic_density=0.1,
traffic_mode=TrafficMode.
Hybrid, # "Respawn", "Trigger", "Hybrid"
random_traffic=True, # Traffic is randomized at default.
# ===== Cost Scheme =====
crash_vehicle_cost=1.,
crash_object_cost=1.,
out_of_road_cost=1.,
# ===== Reward Scheme =====
# See: https://github.com/decisionforce/pgdrive/issues/283
success_reward=10.0,
out_of_road_penalty=5.0,
crash_vehicle_penalty=5.0,
crash_object_penalty=5.0,
acceleration_penalty=0.0,
driving_reward=1.0,
general_penalty=0.0,
speed_reward=0.5,
use_lateral=False,
gaussian_noise=0.0,
dropout_prob=0.0,
vehicle_config=dict(
wheel_friction=0.8,
# See: https://github.com/decisionforce/pgdrive/issues/297
lidar=dict(num_lasers=240,
distance=50,
num_others=4,
gaussian_noise=0.0,
dropout_prob=0.0),
side_detector=dict(num_lasers=0,
distance=50,
gaussian_noise=0.0,
dropout_prob=0.0),
lane_line_detector=dict(num_lasers=0,
distance=50,
gaussian_noise=0.0,
dropout_prob=0.0),
# Following the examples: https://docs.panda3d.org/1.10/python/programming/physics/bullet/vehicles
max_engine_force=1000,
max_brake_force=100,
max_steering=40,
max_speed=80,
),
map_config=dict(block_type_version="v2"),
# Disable map loading!
auto_termination=False,
load_map_from_json=False,
_load_map_from_json="",
))
config.remove_keys([])
return config