def __init__(self,
obs_noise=0.,
x_actions=[0.,0.5,3.],
y_actions=[0,1],
driver_sigma = 0.,
control_cost=0.01,
collision_cost=2.,
survive_reward=0.01,
goal_reward=2.,
road=Road([RoadSegment([(-100.,0.),(100.,0.),(100.,8.),(-100.,8.)])]),
left_bound = -30.,
right_bound = 30.,
gap_min = 8.,
gap_max = 12.,
max_veh_num = 12,
num_updates=1,
dt=0.1,
**kwargs):
self.obs_noise = obs_noise
self.x_actions = x_actions
self.y_actions = y_actions
# we use target value instead of target change so system is Markovian
self.rl_actions = list(itertools.product(x_actions,y_actions))
self.num_updates = num_updates
self.control_cost = control_cost
self.collision_cost = collision_cost
self.survive_reward = survive_reward
self.goal_reward = goal_reward
self.left_bound = left_bound
self.right_bound = right_bound
self.gap_min = gap_min
self.gap_max = gap_max
self.max_veh_num = max_veh_num
self.label_dim = 2
self.label_num = self.max_veh_num
self._collision = False
self._goal = False
self._intentions = []
self._lower_lane_next_idx = 1
self._upper_lane_next_idx = int(self.max_veh_num/2.)+1
self.car_length = 5.0
self.car_width = 2.0
self.car_max_accel = 5.0
self.car_max_speed = 5.0
self.car_max_rotation = 0.
self.car_expose_level = 4
self.driver_sigma = driver_sigma
self.min_front_x = 6.
self.min_back_x = 6.
super(HighWay, self).__init__(
road=road,
cars=[],
drivers=[],
dt=dt,
**kwargs,)
def __init__(self,
yld=0.5,
vs_actions=[0.,0.5,3.],
t_actions=[-1.5,0.,1.5],
desire_speed=3.,
speed_cost=0.01,
t_cost=0.01,
control_cost=0.01,
collision_cost=1.,
outroad_cost=1.,
goal_reward=1.,
road=Road([RoadSegment([(-100.,0.),(100.,0.),(100.,8.),(-100.,8.)]),
RoadSegment([(-2,-10.),(2,-10.),(2,0.),(-2,0.)])]),
n_cars=3,
num_updates=1,
dt=0.1,
**kwargs):
self.yld = yld
self.vs_actions = vs_actions
self.t_actions = t_actions
# we use target value instead of target change so system is Markovian
self.rl_actions = list(itertools.product(vs_actions,t_actions))
self.num_updates = num_updates
self.desire_speed = desire_speed
self.speed_cost = speed_cost
self.t_cost = t_cost
self.control_cost = control_cost
self.collision_cost = collision_cost
self.outroad_cost = outroad_cost
self.goal_reward = goal_reward
self._collision = False
self._outroad = False
self._goal = False
self._intentions = []
car_length=5.0
car_width=2.0
car_max_accel=10.0
car_max_speed=40.0
car_expose_level=4
cars = [Car(idx=cid, length=car_length, width=car_width, color=random.choice(BLUE_COLORS),
max_accel=car_max_accel, max_speed=car_max_speed,
expose_level=car_expose_level) for cid in range(n_cars)
]
driver_sigma = 0.0
s_min = 2.0
min_overlap = 1.0
drivers = []
drivers.append(EgoDriver(trajectory=EgoTrajectory(),idx=0,car=cars[0],dt=dt))
for did in range(1,n_cars):
driver = TwoTDriver(idx=did, car=cars[did], dt=dt,
x_driver=IDMDriver(idx=did, car=cars[did], sigma=driver_sigma, s_min=s_min, axis=0, min_overlap=min_overlap, dt=dt),
y_driver=PDDriver(idx=did, car=cars[did], sigma=driver_sigma, axis=1, dt=dt))
drivers.append(driver)
super(TIntersection, self).__init__(
road=road,
cars=cars,
drivers=drivers,
dt=dt,
**kwargs,)
def __init__(self,
yld=0.5,
obs_noise=0.,
v_noise=0.,
vs_actions=[0., 0.5, 3.],
t_actions=[0.],
desire_speed=3.,
driver_sigma=0.,
speed_cost=0.01,
t_cost=0.01,
control_cost=0.01,
collision_cost=2.,
outroad_cost=2.,
survive_reward=0.01,
goal_reward=2.,
road=Road([
RoadSegment([(-100., 0.), (100., 0.), (100., 8.),
(-100., 8.)]),
RoadSegment([(-2, -10.), (2, -10.), (2, 0.), (-2, 0.)])
]),
left_bound=-20.,
right_bound=20.,
gap_min=3.,
gap_max=10.,
max_veh_num=12,
num_updates=1,
dt=0.1,
des_front_gap_difference=0.1,
des_front_gap_interval=0.3,
**kwargs):
self.yld = yld
self.obs_noise = obs_noise
self.v_noise = v_noise
self.vs_actions = vs_actions
self.t_actions = t_actions
# we use target value instead of target change so system is Markovian
self.rl_actions = list(itertools.product(vs_actions, t_actions))
self.num_updates = num_updates
self.des_front_gap_difference = des_front_gap_difference
self.des_front_gap_interval = des_front_gap_interval
self.desire_speed = desire_speed
self.speed_cost = speed_cost
self.t_cost = t_cost
self.control_cost = control_cost
self.collision_cost = collision_cost
self.outroad_cost = outroad_cost
self.survive_reward = survive_reward
self.goal_reward = goal_reward
self.left_bound = left_bound
self.right_bound = right_bound
self.gap_min = gap_min
self.gap_max = gap_max
self.max_veh_num = max_veh_num
self.label_dim = 2
self.label_num = self.max_veh_num
self._collision = False
self._outroad = False
self._goal = False
self._intentions = []
self._lower_lane_next_idx = 1
self._upper_lane_next_idx = int(self.max_veh_num / 2.) + 1
self.car_length = 5.0
self.car_width = 2.0
self.car_max_accel = 10.0
self.car_max_speed = 40.0
self.car_max_rotation = np.pi / 18.
self.car_expose_level = 4
self.driver_sigma = driver_sigma
self.s_des = 3.0
self.s_min = 3.0
self.min_overlap = 1.0
super(TIntersectionLSTM, self).__init__(
road=road,
cars=[],
drivers=[],
dt=dt,
**kwargs,
)
def __init__(self,
obs_noise=0.,
x_actions=[-5, -1., 0., 1., 5.],
y_actions=[0, 1],
driver_sigma=0.,
x_cost=0.01,
y_cost=0.01,
control_cost=0.01,
collision_cost=2.,
survive_reward=0.01,
goal_reward=2.,
road=Road([
RoadSegment([(-100., 0.), (100., 0.), (100., 12.),
(-100., 12.)])
]),
num_updates=1,
dt=0.1,
**kwargs):
self.obs_noise = obs_noise
self.x_actions = x_actions
self.y_actions = y_actions
# we use target value instead of target change so system is Markovian
self.rl_actions = list(itertools.product(x_actions, y_actions))
self.num_updates = num_updates
self.x_cost = x_cost
self.y_cost = y_cost
self.control_cost = control_cost
self.collision_cost = collision_cost
self.survive_reward = survive_reward
self.goal_reward = goal_reward
self.left_bound = -30.
self.right_bound = 30.
self.x_start = -20.
self.x_goal = 20.
self.lane_start = 0
self.lane_goal = 1
self.gap_min = 6. # 8.
self.gap_max = 10. # 12.
self.max_veh_num = 12 #18
self.label_dim = 2
self.label_num = self.max_veh_num
self._collision = False
self._block = False
self._goal = False
self._terminal = False
self._intentions = []
self._empty_indices = list(range(1, self.max_veh_num + 1))
self.car_length = 5.0
self.car_width = 2.0
self.car_max_accel = 5.0
self.car_max_speed = 1.0
self.ego_max_speed = 2.0
self.car_max_rotation = 0.
self.car_expose_level = 4
self.driver_sigma = driver_sigma
self.min_x = 6.
self.min_y = 3.
self.ego_min_x = 6.
self.ego_min_y = 3.
super(HighWay, self).__init__(
road=road,
cars=[],
drivers=[],
dt=dt,
**kwargs,
)
car.update_render(camera_center)
self.update_extra_render(extra_input)
return self.viewer.render(return_rgb_array=mode == 'rgb_array')
def close(self):
if hasattr(self, 'viewer') and self.viewer:
self.viewer.close()
self.viewer = None
if __name__ == '__main__':
import time
road = Road(
[RoadSegment([(-20., -1.5), (100, -1.5), (100, 7.5), (-20, 7.5)])])
dt = 0.1
n_cars = 2
car_class = Car
driver_class = XYSeperateDriver
x_driver_class = IDMDriver
y_driver_class = PDDriver
driver_sigma = 0.0
car_length = 5.0
car_width = 2.0
car_max_accel = 10.0
car_max_speed = 40.0
car_expose_level = 4
cars = [
car_class(idx=cid,
length=car_length,
def __init__(self,
yld=0.5,
observe_mode='full',
label_mode='full',
normalize_obs=False,
vs_actions=[0., 0.5, 3.],
t_actions=[0.],
desire_speed=3.,
driver_sigma=0.,
speed_cost=0.0,
t_cost=0.0,
control_cost=0.0,
collision_cost=1.,
outroad_cost=1.,
survive_reward=0.0,
goal_reward=1.,
road=Road([
RoadSegment([(-100., 0.), (100., 0.), (100., 8.),
(-100., 8.)]),
RoadSegment([(-2, -10.), (2, -10.), (2, 0.), (-2, 0.)])
]),
left_bound=-20.,
right_bound=20.,
gap_min=3.,
gap_max=10.,
max_veh_num=12,
num_updates=1,
dt=0.1,
**kwargs):
self.yld = yld
self.observe_mode = observe_mode
self.label_mode = label_mode
self.normalize_obs = normalize_obs
self.vs_actions = vs_actions
self.t_actions = t_actions
# we use target value instead of target change so system is Markovian
self.rl_actions = list(itertools.product(vs_actions, t_actions))
self.num_updates = num_updates
self.desire_speed = desire_speed
self.speed_cost = speed_cost
self.t_cost = t_cost
self.control_cost = control_cost
self.collision_cost = collision_cost
self.outroad_cost = outroad_cost
self.survive_reward = survive_reward
self.goal_reward = goal_reward
self._collision = False
self._outroad = False
self._goal = False
self._intentions = []
self.left_bound = left_bound
self.right_bound = right_bound
self.gap_min = gap_min
self.gap_max = gap_max
self.max_veh_num = max_veh_num
self.label_dim = 2
if self.label_mode == 'full':
if observe_mode == 'full':
self.label_num = self.max_veh_num
else:
self.label_num = int(self.max_veh_num / 2) + 1
self.car_length = 5.0
self.car_width = 2.0
self.car_max_accel = 10.0
self.car_max_speed = 40.0
self.car_expose_level = 4
self.driver_sigma = driver_sigma
self.s_des = 3.0
self.s_min = 3.0
self.min_overlap = 1.0
super(TIntersectionExtreme, self).__init__(
road=road,
cars=[],
drivers=[],
dt=dt,
**kwargs,
)
