def __init__(self, env, FPS=50.0):
self.env = env
self.env.reset()
self.car = Car(env.unwrapped.car, 10., 0.)
self.track = env.unwrapped.track
self.dt = 1 / FPS
self.long_term_planning_length = 30
self.short_term_planning_length = 10
def initialize_agent(self):
self.me = Car(self.team, self.index)
self.goals = [Goal(0), Goal(1)]
self.ball = Ball()
if not self.boost_tracker:
self.boost_tracker = BoostTracker()
self.boost_tracker.initialize_all_boost(self.get_field_info())
self.actions = ActionChain()
def update_cars(self, packet: GameTickPacket):
self.allies = [
Car(c.team, i, packet) for i, c in enumerate(packet.game_cars)
if c.team == self.team and i != self.index
]
self.enemies = [
Car(c.team, i, packet) for i, c in enumerate(packet.game_cars)
if c.team != self.team
]
self.me = Car(self.team, self.index, packet)
def run(self, car: Car = None, ball: Ball = None) -> SimpleControllerState:
if self.finished:
return SimpleControllerState()
self.controls = fly_to_target(car, self.target, controls=self.controls)
if car.local(self.target - car.location).length() < 100:
self.finished = True
if self.with_the_quickness:
self.controls.boost = True
return self.controls
def main():
env = gym.make('CarRacing-v0')
done = False
env.reset()
car = env.unwrapped.car
w = car.wheels[0]
dt = 1 / FPS
prev_a = MAX_a
prev_steer = 0
total_reward = 0
for i in range(1000000):
print('########################')
ind = env.unwrapped.tile_visited_count
ind = ind % len(env.unwrapped.track)
target = env.unwrapped.track[ind][2:4]
x, y = car.hull.position
vx, vy = w.linearVelocity * 1
theta = atan2(vy, vx)
dtheta = 0
obs = x, y, theta, vy, vx, dtheta, target
long_term_N = 30
long_term_target = build_long_term_larget(env.unwrapped.track, ind,
(x, y), dt, long_term_N)
ego_car = Car(obs, (prev_a, prev_steer))
t = time()
a, steer, x, y = long_term_MPC(ego_car, long_term_target, dt,
long_term_N)
print('long term solve time:', time() - t)
short_term_N = 5
short_term_target = list(zip(x, y))[:short_term_N]
t = time()
a, steer, x, y = short_term_MPC(ego_car, short_term_target, dt,
short_term_N)
print('short term solve time:', time() - t)
prev_a, prev_steer = a, steer
print(a, steer, np.linalg.norm((vx, vy)))
a = a / MAX_a
steer = steer
if a > 0:
action = steer, a / 10, 0
else:
action = steer, 0, -a
_, r, done, _ = env.step(action)
total_reward += r
# env.render()
print(total_reward)
def run(self, car: Car = None, ball: Ball = None) -> SimpleControllerState:
if self.finished:
return SimpleControllerState()
target = ball.location + self.ball_center_offset
self.controls = drive_to_target(car, target, controls=self.controls)
if car.local(target - car.location).length() < 650:
self.controls.jump = True
self.finished = True
print("Kicked off")
return self.controls
def initialize_target_boost(self, car: Car):
if not car.flying:
if not self.max_time:
self.boost, self.pad = car.get_closest_boosts(
self.boost_tracker, self.in_path)
if not self.boost:
self.boost = self.pad
else:
self.boost, self.pad, times = car.get_closest_boosts(
self.boost_tracker,
in_current_path=self.in_path,
path_angle_limit=0,
return_time_to=True)
# No boost reachable. Life sucks
if times[0] >= self.max_time and times[1] >= self.max_time:
return False
if times[1] < self.max_time:
self.boost = self.pad
print("Boost target acquired!")
self.target = Vec3(self.boost.location)
return True
def run(self, car: Car = None, ball: Ball = None):
# get stopping time in frames
if self.frame == 0:
self.frames_to_stop = car.time_to_stop(coast=False) / dt
# print("parking speed: ", self.speed)
if self.face_ball:
self.controls = drive_to_target(car,
ball.location,
self.controls,
speed=self.speed)
else:
self.controls.throttle = clamp(self.speed)
self.controls.boost = False
self.controls.steer = 0
self.controls.handbrake = False
self.frame += 1
if self.frame >= self.frames_to_stop:
self.finished = True
return self.controls
def run(self, car: Car = None, ball: Ball = None) -> SimpleControllerState:
if self.finished:
return SimpleControllerState()
if not self.boost and self.boost_tracker is not None:
if not self.initialize_target_boost(car):
self.finished = True
# Bail if finished, no boost passed, or boost no longer active
if self.finished or (not self.boost):
return self.controls
self.controls = drive_to_target(car,
self.target.flat(),
controls=self.controls)
# finished if close enough, boost taken, or car got enough along the way
if (car.local(self.target - car.location).length() < 100
or not self.boost.is_active) or car.boost > 99:
print("Grabbed boost!")
self.finished = True
return self.controls
def run(self, car: Car=None, ball: Ball=None) -> SimpleControllerState:
if self.finished:
print("Cops busted me.... Joyride done")
return self.controls
distance_remaining = (self.target-car.location).length()
stop_distance = car.stop_distance(not self.with_the_quickness)
print("distance: ", distance_remaining, " stop distance: ", stop_distance)
# close enough and nowhere to look next
if distance_remaining <= stop_distance:
self.finished = True
self.controls = drive_to_target(car, self.target, controls=self.controls)
# self.controls.throttle = 1 if self.with_the_quickness else 0
# self.controls.boost = self.with_the_quickness
# print(" Finished my joyride")
elif stop_distance < 300:
self.finished = True
self.controls.throttle = 1 if self.with_the_quickness else 0
self.controls.boost = self.with_the_quickness
# print(" Finished my joyride")
else:
self.controls = drive_to_target(car, self.target, controls=self.controls)
return self.controls
def long_term_MPC(ego_car: Car, route_xs, route_ys, dt):
assert len(route_xs) == len(route_ys)
N = len(route_xs)
ego_car_x, ego_car_y, ego_car_v, ego_car_theta, ego_car_a, ego_car_steer = ego_car.get_car_state(
)
x_init = [ego_car_x] * N
y_init = [ego_car_y] * N
theta_init = [ego_car_theta] * N
v_init = [ego_car_v] * N
steer_init = [ego_car_steer] * (N - 1)
a_init = [MAX_a] * (N - 1)
initial_value = x_init + y_init + theta_init + v_init + \
steer_init + a_init
# vars to be optimize
x = SX.sym('x', N)
y = SX.sym('y', N)
theta = SX.sym('theta', N)
v = SX.sym('v', N)
steer = SX.sym('steer', N - 1)
a = SX.sym('a', N - 1)
all_vars = vertcat(x, y, theta, v, steer, a)
# vars upper bound
ub_constrains_x = np.array([ego_car_x] + [INF] * (N - 1))
ub_constrains_y = np.array([ego_car_y] + [INF] * (N - 1))
ub_constrains_theta = np.array([ego_car_theta] + [INF] * (N - 1))
ub_constrains_v = np.array([ego_car_v] + [MAX_v] * (N - 1))
ub_constrains_steer = np.array([ego_car_steer] + [MAX_steer] * (N - 2))
ub_constrains_a = np.array([ego_car_a] + [MAX_a] * (N - 2))
ub_constrains_vars = np.hstack([
ub_constrains_x, ub_constrains_y, ub_constrains_theta, ub_constrains_v,
ub_constrains_steer, ub_constrains_a
])
# vars lower bound
lb_constrains_x = np.array([ego_car_x] + [-INF] * (N - 1))
lb_constrains_y = np.array([ego_car_y] + [-INF] * (N - 1))
lb_constrains_theta = np.array([ego_car_theta] + [-INF] * (N - 1))
lb_constrains_v = np.array([ego_car_v] + [-MIN_v] * (N - 1))
lb_constrains_steer = np.array([ego_car_steer] + [MIN_steer] * (N - 2))
lb_constrains_a = np.array([ego_car_a] + [MIN_a] * (N - 2))
lb_constrains_vars = np.hstack([
lb_constrains_x, lb_constrains_y, lb_constrains_theta, lb_constrains_v,
lb_constrains_steer, lb_constrains_a
])
# define constrain function g (variables update equation)
x_constrain = SX.sym('x_constrain', N - 1)
y_constrain = SX.sym('y_constrain', N - 1)
theta_constrain = SX.sym('theta_constrain', N - 1)
v_constrain = SX.sym('v_constrain', N - 1)
SCALE = 0.002
for i in range(N - 1):
theta_diff = atan(tan(steer[i]) / 2) * v[i] * dt * SCALE
# theta_diff = steer[i] * dt
x_constrain[i] = x[i + 1] - (x[i] + v[i] * dt * np.cos(theta[i]))
y_constrain[i] = y[i + 1] - (y[i] + v[i] * dt * np.sin(theta[i]))
theta_constrain[i] = theta[i + 1] - (theta[i] - theta_diff)
v_constrain[i] = v[i + 1] - (v[i] + a[i] * dt)
all_constrain = vertcat(x_constrain, y_constrain, theta_constrain,
v_constrain)
ub_constrains_g = np.zeros([4 * (N - 1)])
lb_constrains_g = np.zeros([4 * (N - 1)])
# define cost function f
cost = 0
for i in range(N):
# deviation
cost += 20 / N**3 * (N - i)**4 * (x[i] - route_xs[i])**2
cost += 20 / N**3 * (N - i)**4 * (y[i] - route_ys[i])**2
# control cost
if i < N - 2:
cost += 5 * N * steer[i]**2
cost += 0.01 * N * a[i]**2
cost += 20 * N * (steer[i + 1] - steer[i])**2
# cost += 0.1 * N * (a[i + 1] - a[i]) ** 2
nlp = {'x': all_vars, 'f': cost, 'g': all_constrain}
S = nlpsol('S', 'ipopt', nlp, {
"print_time": False,
"ipopt": {
"print_level": 0
}
})
result = S(x0=initial_value,
lbg=lb_constrains_g,
ubg=ub_constrains_g,
lbx=lb_constrains_vars,
ubx=ub_constrains_vars)
# def print_result():
# print('route_x', [i[0] for i in route])
# print('x', result['x'][0:N])
# print('route_y', [i[1] for i in route])
# print('y', result['x'][N:2 * N])
#
# print('theta', result['x'][2 * N:3 * N])
# print('v', result['x'][3 * N:4 * N])
# print('vx', result['x'][3 * N:4 * N] * np.cos(result['x'][2 * N:3 * N]))
# print('vy', result['x'][3 * N:4 * N] * np.sin(result['x'][2 * N:3 * N]))
#
# print('steer', result['x'][4 * N:5 * N - 1])
# print('a', result['x'][5 * N - 1:6 * N - 2])
# print_result()
xs = result['x'][0:N].elements()
ys = result['x'][N:2 * N].elements()
steer = float(result['x'][4 * N + 1])
a = float(result['x'][5 * N])
a = a / MAX_a
if a > 0:
control = Control(steer, a / 10, 0)
else:
control = Control(steer, 0, -a)
# controls = []
# for a, steer in zip(result['x'][5 * N - 1:6 * N - 2].elements(), result['x'][4 * N:5 * N - 1].elements()):
# # steer = float(result['x'][4 * N + 1])
# # a = float(result['x'][5 * N])
# a = a / MAX_a
# if a > 0:
# controls.append(Control(steer, a / 10, 0))
# else:
# controls.append(Control(steer, 0, -a))
return control, xs, ys
def short_term_MPC(ego_car: Car, route_xs, route_ys, dt, verbose=False):
assert len(route_xs) == len(route_ys)
N = len(route_xs)
ego_car_x, ego_car_y, ego_car_v, ego_car_theta, ego_car_a, ego_car_steer = ego_car.get_car_state(
)
x_init = [ego_car_x] * N
y_init = [ego_car_y] * N
theta_init = [ego_car_theta] * N
v_init = [ego_car_v] * N
steer_init = [ego_car_steer] * (N - 1)
a_init = [MAX_a] * (N - 1)
initial_value = x_init + y_init + theta_init + v_init + \
steer_init + a_init
# vars to be optimize
x = SX.sym('x', N)
y = SX.sym('y', N)
theta = SX.sym('theta', N)
v = SX.sym('v', N)
steer = SX.sym('steer', N - 1)
a = SX.sym('a', N - 1)
all_vars = vertcat(x, y, theta, v, steer, a)
# vars upper bound
ub_constrains_x = np.array([ego_car_x] + [INF] * (N - 1))
ub_constrains_y = np.array([ego_car_y] + [INF] * (N - 1))
ub_constrains_theta = np.array([ego_car_theta] + [INF] * (N - 1))
ub_constrains_v = np.array([ego_car_v] + [MAX_v] * (N - 1))
ub_constrains_steer = np.array([ego_car_steer] + [MAX_steer] * (N - 2))
ub_constrains_a = np.array([ego_car_a] + [MAX_a] * (N - 2))
ub_constrains_vars = np.hstack([
ub_constrains_x, ub_constrains_y, ub_constrains_theta, ub_constrains_v,
ub_constrains_steer, ub_constrains_a
])
# vars lower bound
lb_constrains_x = np.array([ego_car_x] + [-INF] * (N - 1))
lb_constrains_y = np.array([ego_car_y] + [-INF] * (N - 1))
lb_constrains_theta = np.array([ego_car_theta] + [-INF] * (N - 1))
lb_constrains_v = np.array([ego_car_v] + [-MIN_v] * (N - 1))
lb_constrains_steer = np.array([ego_car_steer] + [MIN_steer] * (N - 2))
lb_constrains_a = np.array([ego_car_a] + [MIN_a] * (N - 2))
lb_constrains_vars = np.hstack([
lb_constrains_x, lb_constrains_y, lb_constrains_theta, lb_constrains_v,
lb_constrains_steer, lb_constrains_a
])
# define constrain function g (variables update equation)
x_constrain = SX.sym('x_constrain', N - 1)
y_constrain = SX.sym('y_constrain', N - 1)
theta_constrain = SX.sym('theta_constrain', N - 1)
v_constrain = SX.sym('v_constrain', N - 1)
SCALE = 0.002
for i in range(N - 1):
# theta_diff = atan(tan(steer[i]) / 2) * v[i] * dt * SCALE
theta_diff = steer[i] * v[i] * dt * SCALE
x_constrain[i] = x[i + 1] - (x[i] + v[i] * dt * np.cos(theta[i]))
y_constrain[i] = y[i + 1] - (y[i] + v[i] * dt * np.sin(theta[i]))
theta_constrain[i] = theta[i + 1] - (theta[i] - theta_diff)
v_constrain[i] = v[i + 1] - (v[i] + a[i] * dt)
all_constrain = vertcat(x_constrain, y_constrain, theta_constrain,
v_constrain)
ub_constrains_g = np.zeros([4 * (N - 1)])
lb_constrains_g = np.zeros([4 * (N - 1)])
# define cost function f
cost = 0
for i in range(N):
# deviation
cost += 20 * N * (x[i] - route_xs[i])**2
cost += 20 * N * (y[i] - route_ys[i])**2
if i < N - 2:
cost += 1 * N * steer[i]**2
# cost += 0.01 * N * a[i] ** 2
cost += 50 * N * (steer[i + 1] - steer[i])**2
nlp = {'x': all_vars, 'f': cost, 'g': all_constrain}
S = nlpsol('S', 'ipopt', nlp, {
"print_time": False,
"ipopt": {
"print_level": 0
}
})
result = S(x0=initial_value,
lbg=lb_constrains_g,
ubg=ub_constrains_g,
lbx=lb_constrains_vars,
ubx=ub_constrains_vars)
steer = float(result['x'][4 * N + 1])
a = float(result['x'][5 * N])
a = a / MAX_a
if a > 0:
action = Control(steer, a / 10, 0)
else:
action = Control(steer, 0, -a)
if verbose:
xs = result['x'][0:N].elements()
ys = result['x'][N:2 * N].elements()
pprint(xs)
pprint(ys)
return action, None, None
def get_steer(car: Car, target: Vec3):
local_target = car.local(target)
local_target_norm = local_target.normalized()
turn_angle = math.atan2(local_target_norm.y, local_target_norm.x)
return turn_angle
class Environment:
def __init__(self, env, FPS=50.0):
self.env = env
self.env.reset()
self.car = Car(env.unwrapped.car, 10., 0.)
self.track = env.unwrapped.track
self.dt = 1 / FPS
self.long_term_planning_length = 30
self.short_term_planning_length = 10
def render(self):
self.env.render()
def observe(self):
x, y = self.car.get_position()
vx, vy = self.car.get_wheel().linearVelocity * 1
theta = atan2(vy, vx)
return x, y, vx, vy, theta
def step(self, control: Control):
obs = self.observe()
_, reward, done, info = self.env.step(control.to_tuple())
self.car.take_control(control)
return obs, reward, done, info
def seed(self, seed_val):
self.env.seed(seed_val)
def reset(self):
state = self.env.reset()
self.car = Car(self.env.unwrapped.car, 10, 0)
return state
def get_car(self):
return self.car
def get_current_waypoint_index(self):
ind = self.env.unwrapped.tile_visited_count
ind = ind % len(self.get_track())
return ind
def get_track(self):
return self.env.unwrapped.track
def calc_long_term_targets(self):
ind = self.get_current_waypoint_index()
track = self.get_track()
pos = self.car.get_position()
desired_v = 60
dist_travel = desired_v * self.dt
def get_point(start, end, d_to_go):
x0, y0 = start
x1, y1 = end
dy = y1 - y0
dx = x1 - x0
d = np.linalg.norm((dx, dy))
x = x0 + d_to_go * dx / d
y = y0 + d_to_go * dy / d
return x, y
cur_pos = np.array(pos)
cur_target = np.array(track[ind][2:4])
# result = [pos]
xs, ys = [pos[0]], [pos[1]]
for i in range(self.long_term_planning_length - 1):
remain_dist = np.linalg.norm(cur_target - cur_pos) - dist_travel
if remain_dist > 0:
p = get_point(cur_pos, cur_target, dist_travel)
# result.append(p)
xs.append(p[0])
ys.append(p[1])
cur_pos = p
else:
# must ensure distance between 2 target points larger than dist_travel
cur_pos = cur_target
ind = (ind + 1) % len(track)
cur_target = np.array(track[ind][2:4])
p = get_point(cur_pos, cur_target, -remain_dist)
xs.append(p[0])
ys.append(p[1])
cur_pos = p
return xs, ys
def reset(self):
state = self.env.reset()
self.car = Car(self.env.unwrapped.car, 10, 0)
return state
class Dingus(BaseAgent):
def __init__(self, name, team, index):
super().__init__(name, team, index)
self.allies: list[Car] = []
self.enemies: list[Car] = []
self.me: Car = None
self.goals: list[Goal] = None
self.ball: Ball = None
self.actions: ActionChain = None
self.game_info: GameInfo = None
self.boost_tracker: BoostTracker = None
self.ready_for_kickoff: bool = False
self.ball_prediction = None
# self.state = "parked"
self.just_got_boost = False
self.debug_targets = []
def initialize_agent(self):
self.me = Car(self.team, self.index)
self.goals = [Goal(0), Goal(1)]
self.ball = Ball()
if not self.boost_tracker:
self.boost_tracker = BoostTracker()
self.boost_tracker.initialize_all_boost(self.get_field_info())
self.actions = ActionChain()
# Taken from GoslingUtils
def debug_actions(self, only_current=True):
white_color = self.renderer.white()
blue_color = self.renderer.blue()
red_color = self.renderer.red()
yellow_color = self.renderer.yellow()
names = self.actions.get_chain_names()
self.renderer.draw_string_2d(10, 50, 3, 3, self.actions.last_state,
blue_color)
if len(names) > 0:
for i in range(len(names)):
self.renderer.draw_string_2d(10, 50 + (50 * (len(names) - i)),
3, 3, names[i], white_color)
if hasattr(
self.actions.action_list[i], "target"
) and self.actions.action_list[i].target is not None:
target_end = Vec3(
self.actions.action_list[i].target.x,
self.actions.action_list[i].target.y,
self.actions.action_list[i].target.z + 300)
if i == 0:
self.renderer.draw_line_3d(
self.actions.action_list[i].target, target_end,
yellow_color)
else:
self.renderer.draw_line_3d(
self.actions.action_list[i].target, target_end,
red_color)
def line(self, start, end, color=None, alpha=255):
color = color if color is not None else [255, 255, 255]
self.renderer.draw_line_3d(Vec3(start), Vec3(end),
self.renderer.create_color(alpha, *color))
def is_kickoff(self):
ret = self.game_info.is_kickoff_pause and self.game_info.is_round_active
if ret:
self.ready_for_kickoff = True
return ret
def reset_for_kickoff(self):
self.actions = ActionChain(action_list=[BaseKickoff()])
self.debug_targets = []
# self.state = "kickoff"
def update_cars(self, packet: GameTickPacket):
self.allies = [
Car(c.team, i, packet) for i, c in enumerate(packet.game_cars)
if c.team == self.team and i != self.index
]
self.enemies = [
Car(c.team, i, packet) for i, c in enumerate(packet.game_cars)
if c.team != self.team
]
self.me = Car(self.team, self.index, packet)
def preprocess(self, packet: GameTickPacket):
if packet.num_cars != len(self.allies) + len(self.enemies) + 1:
self.update_cars(packet)
[c.update(packet) for c in self.allies]
[c.update(packet) for c in self.enemies]
self.me.update(packet)
self.ball.update(packet)
self.game_info = packet.game_info
self.boost_tracker.update(packet)
# def increment_state(self):
# if len(self.future_actions) < 1:
# self.current_action = None
# elif self.current_action.finished:
# self.current_action = self.future_actions[0]
# del self.future_actions[0]
# else:
# self.current_action = None
# self.future_actions = []
def get_output(self, packet: GameTickPacket) -> SimpleControllerState:
# Preliminary things to do
self.preprocess(packet)
self.renderer.begin_rendering()
self.debug_actions(False)
out: SimpleControllerState = SimpleControllerState()
# self.ball_prediction = self.get_ball_prediction_struct()
# print(self.actions)
# Draw some shit
self.line(self.me.location, self.ball.location, [0, 255, 0])
# self.state = self.actions.last_state
# No current plans
"""
If enemy onside, go back post and wait if not doing either yet
if enemy offside, grab boost if you need it, otherwise ball chase.
boost if the enemy is closer to the ball
Should break routine to go back post if enemy goes onside
"""
enemy_onside = self.enemies[0].onside(self.ball.location, 200)
# Maybe get fancy by checking if this is true in 2 seconds????
back_post = self.goals[self.team].get_back_post_rotation(
self.ball.location)
back_boost = self.boost_tracker.get_back_boost(self.me.side,
-self.ball.side)
closer_to_ball = self.me.local(
self.ball.location -
self.me.location).length() < self.enemies[0].local(
self.ball.location - self.enemies[0].location).length()
print("Dingus closer to ball: ", closer_to_ball)
if not self.actions.busy:
if not enemy_onside or self.actions.last_state != "defending":
if self.me.boost < 25 and self.actions.last_state != "grabbing boost":
self.actions.append(
BoostGrab(boost=None,
boost_tracker=self.boost_tracker))
else:
self.actions.append(
Ballchase(self.ball.last_touch_time,
with_the_quickness=not closer_to_ball))
# print(self.state == "going to defend")
elif enemy_onside:
# if going to defend, this has already been called. if defending, already backpost
print("actions.state: ", self.actions.last_state)
print("action.state != defending: ",
self.actions.last_state != "defending")
print("action.state != going to defend: ",
self.actions.last_state != "going to defend")
if self.actions.last_state != "going to defend" and self.actions.last_state != "defending":
print("Adding another joyride???")
self.go_back_post(boost_first=True)
# check for kickoff reset right before running actions
if self.is_kickoff() and self.ready_for_kickoff:
self.reset_for_kickoff()
if self.actions.busy:
print("doing action: ", self.actions.current.__class__.__name__)
if hasattr(self.actions.current,
"target") and self.actions.current.target is not None:
self.line(self.me.location, self.actions.current.target,
[255, 0, 0])
out = self.actions.execute(packet, car=self.me, ball=self.ball)
# final things to do
self.renderer.end_rendering()
return out
def make_a_plan(self):
pass
def go_back_post(self, boost_first=True):
back_post = self.goals[self.team].get_back_post_rotation(
self.ball.location)
back_post_sign = self.goals[self.team].get_back_post_sign(
self.ball.location)
back_boost = self.boost_tracker.get_back_boost(self.me.side,
back_post_sign)
back_boost_prep_target = Vec3(
back_boost.location.x + (back_post_sign * 250),
back_boost.location.y - (self.me.side * 250), 0)
self.actions.interrupt_now()
if boost_first:
self.actions.append(
Joyride("going to defend",
target=back_boost_prep_target,
with_the_quickness=False))
self.actions.append(
BoostGrab(boost=back_boost, state="going to defend"))
self.actions.append(Joyride("going to defend", target=back_post))
self.actions.append(
Park("defending", face_ball=False, apply_break=False))
def liftoff_angles(car: Car, target:Vec3):
local_target = car.local(target)
yaw = math.atan2(local_target.y, local_target.x)
pitch = math.atan2(local_target.x, local_target.z)
return yaw, pitch
