def world3(flag=False):
dyn = dynamics.CarDynamics(0.1)
world = World()
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1), clane.shifted(-1)]
world.roads += [clane]
world.fences += [
clane.shifted(2),
clane.shifted(-2),
clane.shifted(2.5),
clane.shifted(-2.5)
]
world.cars.append(
car.UserControlledCar(dyn, [0., 0., math.pi / 2., 0.3], color='red'))
world.cars.append(
car.NestedOptimizerCar(dyn, [0., 0.3, math.pi / 2., 0.3],
color='yellow'))
world.cars[1].human = world.cars[0]
world.cars[0].bounds = [(-3., 3.), (-1., 1.)]
if flag:
world.cars[0].follow = world.cars[1].traj_h
r_h = world.simple_reward([
world.cars[1].traj
]) + 100. * feature.bounded_control(world.cars[0].bounds)
@feature.feature
def human(t, x, u):
return (world.cars[1].traj_h.x[t][0]) * 10
r_r = 300. * human + world.simple_reward(world.cars[1], speed=0.5)
world.cars[1].rewards = (r_h, r_r)
#world.objects.append(Object('firetruck', [0., 0.7]))
return world
def get_control_reward(self, fw):
"""Compute the control reward."""
control_r = (self.w_control * feature.control())
bounded_control_r = (
self.w_bounded_control *
feature.bounded_control(fw, self.car_control_bounds))
return control_r + bounded_control_r
def world1(flag=False):
dyn = dynamics.CarDynamics(0.1)
world = World()
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1), clane.shifted(-1)]
world.roads += [clane]
world.fences += [clane.shifted(2), clane.shifted(-2)]
world.cars.append(
car.UserControlledCar(dyn, [-0.13, 0., math.pi / 2., 0.3],
color='red'))
world.cars.append(
car.NestedOptimizerCar(dyn, [0.0, 0.5, math.pi / 2., 0.3],
color='yellow'))
world.cars[1].human = world.cars[0]
if flag:
world.cars[0].follow = world.cars[1].traj_h
r_h = world.simple_reward(
[world.cars[1].traj],
speed_import=.2 if flag else 1.,
speed=0.8
if flag else 1.) + 100. * feature.bounded_control(world.cars[0].bounds)
@feature.feature
def human_speed(t, x, u):
return -world.cars[1].traj_h.x[t][3]**2
r_r = 300. * human_speed + world.simple_reward(world.cars[1], speed=0.5)
if flag:
world.cars[0].follow = world.cars[1].traj_h
world.cars[1].rewards = (r_h, r_r)
#world.objects.append(Object('cone', [0., 1.8]))
return world
def assign_nest_goal(self, acting_car, passive_car, acting_car_reward):
if passive_car is None:
acting_car.nested = False
acting_car.simple_reward = acting_car_reward
else:
acting_car.nested = True
acting_car.human = passive_car
# create a list of all the trajectories that the human is doing collision avoidance with.
# use true (not linear) trajectory for the robot car, since it is a Stackelberg game.
# TODO: only use the cars close to the human
trajs_to_avoid = []
for ca in self.cars:
if ca is acting_car:
trajs_to_avoid.append(ca.traj)
elif ca is not passive_car:
trajs_to_avoid.append(ca.linear)
hum_reward = self.simple_reward(
trajs_to_avoid) + 100. * feature.bounded_control(
passive_car.bounds)
acting_car.nested_rewards = (hum_reward, acting_car_reward)
def world0():
dyn = dynamics.CarDynamics(0.1)
world = World()
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1), clane.shifted(-1)]
world.roads += [clane]
world.fences += [clane.shifted(2), clane.shifted(-2)]
world.cars.append(
car.UserControlledCar(dyn, [-0.13, 0., math.pi / 2., 0.3],
color='red'))
world.cars.append(
car.NestedOptimizerCar(dyn, [0.0, 0.5, math.pi / 2., 0.3],
color='yellow'))
world.cars[1].human = world.cars[0]
r_h = world.simple_reward([
world.cars[1].traj
]) + 100. * feature.bounded_control(world.cars[0].bounds)
@feature.feature
def human_speed(t, x, u):
return -world.cars[1].traj_h.x[t][3]**2
r_r = world.simple_reward(world.cars[1], speed=0.5)
world.cars[1].rewards = (r_h, r_r)
return world
def world6(know_model=True):
dyn = dynamics.CarDynamics(0.1)
world = World()
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1), clane.shifted(-1)]
world.roads += [clane]
world.fences += [clane.shifted(2), clane.shifted(-2), clane.shifted(2.5), clane.shifted(-2.5)]
world.cars.append(car.SimpleOptimizerCar(dyn, [-0.13, 0., math.pi/2., 0.5], color='red'))
if know_model:
world.cars.append(car.NestedOptimizerCar(dyn, [0., 0.05, math.pi/2., 0.5], color='yellow'))
else:
world.cars.append(car.SimpleOptimizerCar(dyn, [0., 0.05, math.pi/2., 0.5], color='yellow'))
world.cars[0].reward = world.simple_reward(world.cars[0], speed=0.6)
world.cars[0].default_u = np.asarray([0., 1.])
@feature.feature
def goal(t, x, u):
return -(10.*(x[0]+0.13)**2+0.5*(x[1]-2.)**2)
if know_model:
world.cars[1].human = world.cars[0]
r_h = world.simple_reward([world.cars[1].traj], speed=0.6)+100.*feature.bounded_control(world.cars[0].bounds)
r_r = 10*goal+world.simple_reward([world.cars[1].traj_h], speed=0.5)
world.cars[1].rewards = (r_h, r_r)
else:
r = 10*goal+world.simple_reward([world.cars[0].linear], speed=0.5)
world.cars[1].reward = r
return world
def world0():
dyn = dynamics.CarDynamics(0.1)
world = World()
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1), clane.shifted(-1)]
world.roads += [clane]
world.fences += [clane.shifted(2), clane.shifted(-2)]
world.cars.append(car.UserControlledCar(dyn, [-0.13, 0., math.pi/2., 0.3], color='red'))
world.cars.append(car.NestedOptimizerCar(dyn, [0.0, 0.5, math.pi/2., 0.3], color='yellow'))
world.cars[1].human = world.cars[0]
r_h = world.simple_reward([world.cars[1].traj])+100.*feature.bounded_control(world.cars[0].bounds)
@feature.feature
def human_speed(t, x, u):
return -world.cars[1].traj_h.x[t][3]**2
r_r = world.simple_reward(world.cars[1], speed=0.5)
world.cars[1].rewards = (r_h, r_r)
return world
def world3(flag=False):
dyn = dynamics.CarDynamics(0.1)
world = World()
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1), clane.shifted(-1)]
world.roads += [clane]
world.fences += [clane.shifted(2), clane.shifted(-2), clane.shifted(2.5), clane.shifted(-2.5)]
world.cars.append(car.UserControlledCar(dyn, [0., 0., math.pi/2., 0.3], color='red'))
world.cars.append(car.NestedOptimizerCar(dyn, [0., 0.3, math.pi/2., 0.3], color='yellow'))
world.cars[1].human = world.cars[0]
world.cars[0].bounds = [(-3., 3.), (-1., 1.)]
if flag:
world.cars[0].follow = world.cars[1].traj_h
r_h = world.simple_reward([world.cars[1].traj])+100.*feature.bounded_control(world.cars[0].bounds)
@feature.feature
def human(t, x, u):
return (world.cars[1].traj_h.x[t][0])*10
r_r = 300.*human+world.simple_reward(world.cars[1], speed=0.5)
world.cars[1].rewards = (r_h, r_r)
#world.objects.append(Object('firetruck', [0., 0.7]))
return world
def world4(flag=False):
dyn = dynamics.CarDynamics(0.1)
world = World()
vlane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
hlane = lane.StraightLane([-1., 0.], [1., 0.], 0.13)
world.lanes += [vlane, hlane]
world.fences += [hlane.shifted(-1), hlane.shifted(1)]
world.cars.append(
car.UserControlledCar(dyn, [0., -.3, math.pi / 2., 0.0], color='red'))
world.cars.append(
car.NestedOptimizerCar(dyn, [-0.3, 0., 0., 0.], color='yellow'))
world.cars[1].human = world.cars[0]
world.cars[0].bounds = [(-3., 3.), (-2., 2.)]
if flag:
world.cars[0].follow = world.cars[1].traj_h
world.cars[1].bounds = [(-3., 3.), (-2., 2.)]
@feature.feature
def horizontal(t, x, u):
# isn't x[2] the heading, not the y location?
return -x[2]**2
r_h = world.simple_reward(
[world.cars[1].traj],
lanes=[vlane],
fences=[vlane.shifted(-1), vlane.shifted(1)] *
2) + 100. * feature.bounded_control(world.cars[0].bounds)
@feature.feature
def human(t, x, u):
return -tt.exp(-10 * (world.cars[1].traj_h.x[t][1] - 0.13) / 0.1)
r_r = human * 10. + horizontal * 30. + world.simple_reward(
world.cars[1],
lanes=[hlane] * 3,
fences=[hlane.shifted(-1), hlane.shifted(1)] * 3 +
[hlane.shifted(-1.5), hlane.shifted(1.5)] * 2,
speed=0.9)
world.cars[1].rewards = (r_h, r_r)
return world
def reward(self, reward):
# tar fram reward med hjalp av input och reward fran bounded_control
# bounded_control anvander sig av kollisions boxarna i varlden
self._reward = reward + 100. * feature.bounded_control(self.bounds)
self.optimizer = None # skapar en tom optimizer
def world_kex1(know_model=True):
start_human = -0.13
start_robot = -0.00
dyn = dynamics.CarDynamics(0.1)
world = World()
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1), clane.shifted(-1)]
world.roads += [clane]
world.fences += [
clane.shifted(2),
clane.shifted(-2),
clane.shifted(2.5),
clane.shifted(-2.5)
]
#world.cars.append(car.SimpleOptimizerCar(dyn, [start_human, 0., math.pi/2., 0.5], color='red')) # red car is human
world.cars.append(
car.NestedOptimizerCar(dyn, [start_human, 0., math.pi / 2., 0.5],
color='red')) # red car is human
if know_model: # yellow car is the robot that uses nested optimizer to find the way
world.cars.append(
car.NestedOptimizerCar(dyn, [start_robot, 0.0, math.pi / 2., 0.5],
color='yellow'))
else:
world.cars.append(
car.SimpleOptimizerCar(dyn, [start_robot, 0.0, math.pi / 2., 0.5],
color='yellow'))
world.cars[0].reward = world.simple_reward(world.cars[0], speed=0.6)
world.cars[0].default_u = np.asarray([0., 1.])
@feature.feature
def goal(t, x, u): # doesnt need this
k = -(10. * (x[0] + 0.13)**2 + 0.5 * (x[1] - 2.)**2) #ASK Elis
#print("--------", x[0].auto_name)
#print("--------", x[1].auto_name)
#exit()
return k
# object--------------
world.cars.append(
car.SimpleOptimizerCar(dyn, [-0.13, 0.5, math.pi / 2., 0.0],
color='blue')) # blue car is obstacle
#world.cars.append(car.NestedOptimizerCar(dyn, [-0.13, 0.5, math.pi/2., 0.0], color='blue')) # blue car is obstacle
#print(world.cars)
#exit()
world.cars[2].reward = world.simple_reward(world.cars[2], speed=0.0)
#world.cars[2].reward = 1
world.cars[2].default_u = np.asarray([0., 0.])
world.cars[2].movable = False
#------------------
if know_model:
world.cars[1].human = world.cars[
0] # [1] is robot, asigns that the robot knows who is the human
world.cars[1].obstacle = world.cars[2]
world.cars[0].obstacle = world.cars[2]
world.cars[0].human = world.cars[1]
# reward with respect to the robot trajectory: world.cars[1].traj
r_h = world.simple_reward(
[world.cars[1].traj], speed=0.5) + 100. * feature.bounded_control(
world.cars[0].bounds) + 100. * feature.bounded_control(
world.cars[2].bounds)
#r_r = 10*goal+world.simple_reward([world.cars[1].traj_h], speed=0.5
r_r = world.simple_reward(
[world.cars[1].traj_h],
speed=0.5) + 100. * feature.bounded_control(world.cars[2].bounds)
r_h2 = world.simple_reward(
[world.cars[1].traj_h],
speed=0.5) + 100. * feature.bounded_control(world.cars[0].bounds)
+100. * feature.bounded_control(world.cars[2].bounds)
#r_r = 10*goal+world.simple_reward([world.cars[1].traj_h], speed=0.5
r_r2 = world.simple_reward(
[world.cars[1].traj],
speed=0.5) + 100. * feature.bounded_control(world.cars[2].bounds)
#r_obj = world.simple_reward([world.cars[1].traj_h], speed=0.0)
world.cars[1].rewards = (r_h, r_r) #ADD: r_object
world.cars[0].rewards = (r_h2, r_r2) #(optimize on, the car)
#print(r_h)
#print(r_r)
#print(world.cars[1].rewards)
#exit()
else:
r = 10 * goal + world.simple_reward([world.cars[0].linear], speed=0.5)
world.cars[1].reward = r
#world.cars.append(static_obj.SimpleOptimizerCar(dyn, [-0.13, 0.5, math.pi/2., 0.0], color='blue')) # blue car is obstacle)
return world
def world_kex_old(know_model=True):
dyn = dynamics.CarDynamics2(0.1)
#dyn.dt = 1.0
#dyn.fiction = 0.0
world = World()
# clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
# world.lanes += [clane, clane.shifted(1), clane.shifted(-1)]
# world.roads += [clane]
# world.fences += [clane.shifted(2), clane.shifted(-2), clane.shifted(2.5), clane.shifted(-2.5)]
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1)]
#world.roads += [clane, clane.shifted(1)]
world.fences += [clane.shifted(2), clane.shifted(-1)]
human_is_follower = False
# CAR 0 = Human
# CAR 1 = Robot
# CAR 2 = Obstacle
# IMPORTANT: Folower must be created first
# depending on what our human is, follower or leader we create the cars differently
if human_is_follower:
# Create the cars-----
# Human Car
#world.cars.append(car.NestedOptimizerCarFollower(dyn, [-0.13, 0.0, math.pi/2., 0.5], color='red', T=3))
world.cars.append(
car.NestedOptimizerCarFollower2(dyn,
[-0.13, 0.0, math.pi / 2., 0.5],
color='red',
T=3))
# Robot Car
world.cars.append(
car.NestedOptimizerCarLeader(dyn, [-0., 0., math.pi / 2., 0.5],
color='yellow',
T=3))
#world.cars[0].leader = world.cars[1]
#world.cars[0].leader1 = world.cars[1]
# --------------------
else:
# Create the cars-----
# Human Car
world.cars.append(
car.NestedOptimizerCarFollower2(dyn, [0., 0., math.pi / 2., 0.5],
color='yellow',
T=3))
world.cars.append(
car.NestedOptimizerCarLeader(dyn, [-0.13, 0.0, math.pi / 2., 0.5],
color='red',
T=3))
# Robot Car
#world.cars.append(car.NestedOptimizerCarFollower(dyn, [0., 0., math.pi/2., 0.5], color='yellow', T=3))
#world.cars[1].leader = world.cars[0]
#world.cars[1].leader1 = world.cars[0]
# --------------------
# Obstacle Car
#world.cars.append(car.SimpleOptimizerCar(dyn, [-0.13, 0.5, math.pi/2., 0.5], color='blue')) # doesnt work because it cant force the car to turn around
world.cars.append(
car.SimpleOptimizerCar(dyn, [-0.13, 2, math.pi / 4., 0.],
color='blue'))
# --------------------
# Reward and default for the Human ---
# speed did not change here
# world.cars[0].reward = world.simple_reward(world.cars[0], speed=0.6)
world.cars[0].default_u = np.asarray([0., 1.])
# ------------------------------------
# Reward and default for the Robot ---
# world.cars[1].reward = world.simple_reward(world.cars[1], speed=0.6)
world.cars[1].default_u = np.asarray([0., 1.])
# ------------------------------------
# Reward and default for the Obstacle ---
world.cars[2].reward = world.simple_reward(world.cars[2], speed=0.)
world.cars[2].default_u = np.asarray([0., 0.])
world.cars[2].movable = False
# ------------------------------------
# CAR 0 = Human
# CAR 1 = Robot
# CAR 2 = Obstacle
if human_is_follower:
world.cars[0].leader = world.cars[1]
world.cars[0].obstacle = world.cars[2]
world.cars[1].follower = world.cars[0]
world.cars[1].obstacle = world.cars[2]
else:
world.cars[1].follower = world.cars[0]
world.cars[1].obstacle = world.cars[2]
world.cars[0].leader = world.cars[1]
world.cars[0].obstacle = world.cars[2]
# CAR 0 = Human
# CAR 1 = Robot
# CAR 2 = Obstacle
# TODO: Fix this part, unsure how to make the world.simplereward
# calculates the dynamic(chaning) rewards for the cars depending on their speed and collision with other cars and obstacles
#TODO: this is what is wrong, they need to be the same
# TODO: cars dont want to slow down, find a solution that works
if human_is_follower:
# HUMAN
#r_h = world.simple_reward([world.cars[1].traj], speed=0.6)+100.*feature.bounded_control(world.cars[0].bounds)+world.simple_reward(world.cars[0].traj_o, speed=0.) # Reward for the human
r_h = world.simple_reward(
[world.cars[1].traj], speed=0.80) + 100. * feature.bounded_control(
world.cars[0].bounds) + 1 * world.simple_reward(
world.cars[1].traj_o, speed=0.80) # Reward for the human
# ROBOT
r_r = world.simple_reward(
[world.cars[1].traj_h],
speed=0.8) + 100. * feature.bounded_control(
world.cars[1].bounds) + 1 * world.simple_reward(
world.cars[1].traj_o, speed=0.8) # Reward for the robot
else:
# HUMAN
r_h = world.simple_reward(
[world.cars[1].traj_h],
speed=0.8) + 100. * feature.bounded_control(
world.cars[0].bounds) + 1 * world.simple_reward(
world.cars[1].traj_o, speed=0.8) # Reward for the human
# ROBOT
r_r = world.simple_reward(
[world.cars[1].traj], speed=0.8) + 100. * feature.bounded_control(
world.cars[1].bounds) + 1 * world.simple_reward(
world.cars[1].traj_o, speed=0.8) # Reward for the robot
r_o = 1. * feature.bounded_control(world.cars[2].bounds)
#r_o = world.simple_reward([world.cars[0].traj_o], speed=0.)
world.cars[0].rewards = (r_r, r_h, r_o)
world.cars[1].rewards = (r_h, r_r, r_o)
# ------------------------------------
return world
def move_humans_from_lane(self, lane_idx):
def move_from_lane(rob_car,
desired_xloc,
width=0.5,
lane_width=lane.DEFAULT_WIDTH):
@feature.feature
def f(t, x, u):
return tt.exp(-0.5 *
((rob_car.traj_h.x[t][0] - desired_xloc)**2) /
(width**2 * lane_width * lane_width / 4.))
return f
# collect the cars in the lane of interest
lane_car_idcs = []
for i in range(len(self.cars)):
if (self.veh_lanes[self.cars[i]] == lane_idx):
lane_car_idcs.append(i)
# Order the cars by position
# create a list of the vehicle y-positions in the mixed lane
mixed_veh_ypos = [self.cars[i].x[1] for i in lane_car_idcs]
# now sort these indices according to vehicle y-position
sorted_idcs = np.argsort(np.array(mixed_veh_ypos))
# Now go through these cars and see if there is a human vehicle behind them.
# If so, get the human to leave the lane. If not, if there is a robot in front,
# platoon with them
for i in range(len(sorted_idcs)):
acting_car = self.cars[lane_car_idcs[sorted_idcs[i]]]
# only assign goals if the car is a robot
if acting_car.iamrobot:
# If this isn't the last car in the lane, check behind it
if (i != 0):
if not self.cars[lane_car_idcs[sorted_idcs[i -
1]]].iamrobot:
# then kick the car out
acting_car.nested = True
acting_car.human = self.cars[lane_car_idcs[sorted_idcs[
i - 1]]]
rob_reward = acting_car.baseline_reward + 400. * move_from_lane(
acting_car,
desired_xloc=self.lane_centers[
self.veh_lanes[acting_car] + 1])
#TODO: add speed into here! maybe assume that the current speed is the desired speed??
# create a list of all the trajectories that the human is doing collision avoidance with.
# use true (not linear) trajectory for the robot car, since it is a Stackelberg game.
# TODO: only use the cars close to the human
trajs_to_avoid = []
for ca in self.cars:
if ca is acting_car:
trajs_to_avoid.append(ca.traj)
elif ca is not self.cars[lane_car_idcs[sorted_idcs[
i - 1]]]:
trajs_to_avoid.append(ca.linear)
hum_reward = self.simple_reward(
trajs_to_avoid) + 100. * feature.bounded_control(
acting_car.human.bounds)
acting_car.nested_rewards = (hum_reward, rob_reward)
# if the car in front it is a robot, then be free to platoon (since the car behind it is a robot too)
# check that it is not the front-most car in the lane
elif i < len(mixed_veh_ypos) - 1:
# if there is a robot in front of it, platoon with them
if self.cars[lane_car_idcs[sorted_idcs[i +
1]]].iamrobot:
# Then platoon with the car in front
acting_car.nested = False
acting_car.platoon(
self.cars[lane_car_idcs[sorted_idcs[i + 1]]])
# if it is the rear-most vehicle in a lane, check that it is not the only vehicle in the lane
elif i < len(mixed_veh_ypos) - 1:
if self.cars[lane_car_idcs[sorted_idcs[i + 1]]].iamrobot:
# Then platoon with the car in front
acting_car.nested = False
acting_car.platoon(
self.cars[lane_car_idcs[sorted_idcs[i + 1]]])
def world_kex(know_model=True):
dyn = dynamics.CarDynamics2(0.1)
world = World()
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1)]
#world.roads += [clane, clane.shifted(1)]
world.fences += [clane.shifted(2), clane.shifted(-1)]
# both behind: pos=0.027 (both begind) and pos=0.028 (different behaviours)
# both infront: pos=0.128 (different behaviours) and pos=0.129 (both infront) to switch
# We run:
# T_stepls = 3
# step_per_u = 2
# speed = 0.80
# we have 3 different results:
# 1. both begind
# 2. both infront
# 3. different behaviour depending on role
# to get the distance take the pos/0.13 to get it to irl meters
left_is_follower = False
pos = 0.15
#pos = 0.15
#pos=0.0
T_steps = 3
speed = 0.80
#pos = 0.128
#pos = 0.028
# THIS WORKS
# steps per u is 2
#left_is_follower = False
#T_steps = 3
#pos = 0.10 #WORKS
#speed = 0.80
# Demonstration
left_color = "green"
right_color = "blue-dark"
# Real
#follower_color = "yellow"
#leader_color = "red"
# Follower must alwasy be created first, otherwise it won't move
if left_is_follower:
world.cars.append(
car.NestedOptimizerCarFollower2(dyn,
[-0.13, pos, math.pi / 2., speed],
color=left_color,
T=T_steps))
world.cars.append(
car.NestedOptimizerCarLeader(dyn, [-0.0, 0.0, math.pi / 2., speed],
color=right_color,
T=T_steps))
else:
world.cars.append(
car.NestedOptimizerCarFollower2(dyn,
[-0.0, 0.0, math.pi / 2., speed],
color=right_color,
T=T_steps))
world.cars.append(
car.NestedOptimizerCarLeader(dyn,
[-0.13, pos, math.pi / 2., speed],
color=left_color,
T=T_steps))
#world.cars.append(car.SimpleOptimizerCar(dyn, [-0.13, 2, math.pi/4., 0.], color='blue'))
# THE OBSTACLE IT WORKS WITH
#world.cars.append(car.SimpleOptimizerCar(dyn, [-0.20, 1, math.pi/4., 0.], color='blue'))
# THE OBSTACLE FOR DEMONSTRATIONS
world.cars.append(
car.SimpleOptimizerCar(dyn, [-0.20, 0.7, math.pi / 4., 0.],
color='gray'))
# default_u for the cars
world.cars[0].default_u = np.asarray([0., 1.])
world.cars[1].default_u = np.asarray([0., 1.])
# Reward and default for the Obstacle ---
world.cars[2].reward = world.simple_reward(world.cars[2], speed=0.)
world.cars[2].default_u = np.asarray([0., 0.])
world.cars[2].movable = False
# tells the cars who is the follower and who is the leader
world.cars[0].leader = world.cars[1]
world.cars[1].follower = world.cars[0]
world.cars[0].obstacle = world.cars[2]
world.cars[1].obstacle = world.cars[2]
r_leader = world.simple_reward(
[world.cars[1].traj_h, world.cars[1].traj_o, world.cars[1].traj_o],
speed=speed)
# leader doesnt need bounded controls, only the follower
r_follower = world.simple_reward(
[world.cars[1].traj, world.cars[1].traj_o, world.cars[1].traj_o],
speed=speed) + 100. * feature.bounded_control(world.cars[0].bounds)
r_o = 0.
#r_o = world.simple_reward([world.cars[0].traj_o], speed=0.)
world.cars[0].rewards = (r_leader, r_follower)
world.cars[1].rewards = (r_follower, r_leader)
# ------------------------------------
return world
def world5():
dyn = dynamics.CarDynamics(0.1)
world = World()
vlane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
hlane = lane.StraightLane([-1., 0.], [1., 0.], 0.13)
world.lanes += [vlane, hlane]
world.fences += [hlane.shifted(-1), hlane.shifted(1)]
world.cars.append(car.UserControlledCar(dyn, [0., -.3, math.pi/2., 0.0], color='red'))
world.cars.append(car.NestedOptimizerCar(dyn, [-0.3, 0., 0., 0.0], color='yellow'))
world.cars[1].human = world.cars[0]
world.cars[1].bounds = [(-3., 3.), (-2., 2.)]
@feature.feature
def horizontal(t, x, u):
return -x[2]**2
r_h = world.simple_reward([world.cars[1].traj], lanes=[vlane], fences=[vlane.shifted(-1), vlane.shifted(1)]*2)+100.*feature.bounded_control(world.cars[0].bounds)
@feature.feature
def human(t, x, u):
return -tt.exp(10*(world.cars[1].traj_h.x[t][1]-0.13)/0.1)
r_r = human*10.+horizontal*2.+world.simple_reward(world.cars[1], lanes=[hlane]*3, fences=[hlane.shifted(-1), hlane.shifted(1)]*3+[hlane.shifted(-1.5), hlane.shifted(1.5)]*2, speed=0.9)
world.cars[1].rewards = (r_h, r_r)
return world
def world1(flag=False):
dyn = dynamics.CarDynamics(0.1)
world = World()
clane = lane.StraightLane([0., -1.], [0., 1.], 0.13)
world.lanes += [clane, clane.shifted(1), clane.shifted(-1)]
world.roads += [clane]
world.fences += [clane.shifted(2), clane.shifted(-2)]
world.cars.append(car.UserControlledCar(dyn, [-0.13, 0., math.pi/2., 0.3], color='red'))
world.cars.append(car.NestedOptimizerCar(dyn, [0.0, 0.5, math.pi/2., 0.3], color='yellow'))
world.cars[1].human = world.cars[0]
if flag:
world.cars[0].follow = world.cars[1].traj_h
r_h = world.simple_reward([world.cars[1].traj], speed_import=.2 if flag else 1., speed=0.8 if flag else 1.)+100.*feature.bounded_control(world.cars[0].bounds)
@feature.feature
def human_speed(t, x, u):
return -world.cars[1].traj_h.x[t][3]**2
r_r = 300.*human_speed+world.simple_reward(world.cars[1], speed=0.5)
if flag:
world.cars[0].follow = world.cars[1].traj_h
world.cars[1].rewards = (r_h, r_r)
#world.objects.append(Object('cone', [0., 1.8]))
return world
def reward(self, reward):
self._reward = reward + 100. * feature.bounded_control(self.bounds)
self.optimizer = None
def assign_goals_phase_zero(self):
# make sure we are in the correct phase
assert (self.cur_phase == 0)
# first update our knowledge of where the vehicles are
self.find_veh_lanes()
for c in self.rob_cars:
if (self.veh_lanes[c] == self.rob_cars_lane_asg[c]):
# change the a simple optimizer and remove the car from the list
c.nested = False
# put back to baseline reward
c.simple_reward = c.baseline_reward
# Now with the remaining vehicles, assign them reward functions to get closer to their goals
# TODO: if there is a smart vehicle nearby, just go in front of that one and turn down collision avoidance for smart vehicles
def move_to_lane(desired_xloc,
width=0.5,
lane_width=lane.DEFAULT_WIDTH):
@feature.feature
def f(t, x, u):
return tt.exp(-0.5 * ((x[0] - desired_xloc)**2) /
(width**2 * lane_width * lane_width / 4.))
return f
for c in self.rob_cars:
# if the car is in the right place, make sure it goes back to its baseline reward
if (self.veh_lanes[c] == self.rob_cars_lane_asg[c]):
c.nested = False
# put back to baseline reward
c.simple_reward = c.baseline_reward
else:
if (self.rob_cars_lane_asg[c] > self.veh_lanes[c]):
# Then we want to move the car to the right
# TODO: does this actually change the reward or just in a shallow copy?
#new_reward = c.simple_reward + move_right_reward
new_reward = c.baseline_reward + 100. * move_to_lane(
desired_xloc=self.lane_centers[self.veh_lanes[c] + 1])
move_direction = 1
else:
new_reward = c.baseline_reward + 100. * move_to_lane(
desired_xloc=self.lane_centers[self.veh_lanes[c] - 1])
move_direction = -1
# Find if there is a vehicle blocking -- just pair with the first vehicle behind it in the lane over
#TODO: change this so it can be one car-length in front and still be considered blocking
candidate = None
for cb in self.cars:
if cb.iamrobot:
continue
if self.veh_lanes[
cb] == self.veh_lanes[c] + move_direction:
if (candidate is None) and (cb.x[1] <= c.x[1]):
candidate = cb
else:
# Find which is more blocking -- closer in y-position but still behind
if ((cb.x[1] <= c.x[1])
and (cb.x[1] > candidate.x[1])):
candidate = cb
if candidate is None:
c.nested = False
c.simple_reward = new_reward
else:
c.nested = True
c.human = candidate
# create a list of all the trajectories that the human is doing collision avoidance with.
# use true (not linear) trajectory for the robot car, since it is a Stackelberg game.
# TODO: only use the cars close to the human
trajs_to_avoid = []
for ca in self.cars:
if ca is c:
trajs_to_avoid.append(ca.traj)
elif ca is not candidate:
trajs_to_avoid.append(ca.linear)
#TODO: add speed into simple reward!
hum_reward = self.simple_reward(
trajs_to_avoid) + 100. * feature.bounded_control(
candidate.bounds)
c.nested_rewards = (
hum_reward, new_reward
) # This assumes perfect knowledge of human reward functions
def reward(self, reward):
self._reward = reward+100.*feature.bounded_control(self.bounds)
self.optimizer = None