def play(self, pod: PodState) -> PlayOutput:
check1 = self.board.checkpoints[pod.nextCheckId]
check2 = self.board.get_check(pod.nextCheckId + 1)
c1_to_p = (pod.pos - check1)
c1_to_p_len = c1_to_p.length()
c1_to_c2 = (check2 - check1)
c1_to_c2_len = c1_to_c2.length()
midpoint = ((c1_to_p / c1_to_c2_len) -
(c1_to_c2 / c1_to_c2_len)).normalize()
target = check1
if c1_to_p_len > Constants.max_vel() * 6:
# Still far away. Aim for a point that will help us turn toward the next check
target = target + (midpoint * Constants.check_radius() * 2)
# else: We're getting close to the check. Stop fooling around and go to it.
# OK, now we've got a target point. Do whatever it takes to get there.
pod_to_target = target - pod.pos
ang_diff_to_target = math.fabs(
clean_angle(math.fabs(pod.angle - pod_to_target.angle())))
if ang_diff_to_target < 2 * Constants.max_turn():
thrust = Constants.max_thrust()
elif ang_diff_to_target < 4 * Constants.max_turn():
thrust = (ang_diff_to_target - (4 * Constants.max_turn())) / (
2 * Constants.max_turn()) * Constants.max_thrust()
else:
thrust = 0
return PlayOutput(target - (2 * pod.vel), thrust)
def play_to_action(self, thrust: int, angle: float) -> int:
"""
Given a legal play (angle/thrust), find the nearest discrete action
"""
thrust_pct = thrust / Constants.max_thrust()
angle_pct = (angle + Constants.max_turn()) / (2 * Constants.max_turn())
thrust_idx = math.floor(thrust_pct * (self.num_thrust - 1))
angle_idx = math.floor(angle_pct * (self.num_angle - 1))
return math.floor(thrust_idx * self.num_angle + angle_idx)
def __init__(self, num_thrust: int = 2, num_angle: int = 3):
self.num_thrust = num_thrust
self.num_angle = num_angle
self.num_actions = num_thrust * num_angle
thrusts = _arange(0, Constants.max_thrust(), num_thrust)
angs = _arange(-Constants.max_turn(), Constants.max_turn(), num_angle)
self._action_table = [(int(thr), ang) for thr in thrusts
for ang in angs]
def legal_angle(req_angle: float, pod_angle: float) -> float:
"""
Get the actual angle to apply, given the player's input
:param req_angle: Angle that the player requested
:param pod_angle: Angle in which the pod is facing
:return: Angle to use for calculations (within [-pi, pi])
"""
d_angle = within(clean_angle(req_angle - pod_angle), -Constants.max_turn(),
Constants.max_turn())
return clean_angle(pod_angle + d_angle)
def test_action_to_output_turn_right(self):
action = self.ad.play_to_action(50, Constants.max_turn())
pod_pos = Vec2(100, 100)
po = self.ad.action_to_output(action, 1.23, pod_pos)
# The thrust should not have changed
self.assertEqual(po.thrust, 50)
# The pod is at (100, 100), angle 1.23, requested turn max_turn...
# If we undo the move and rotate, we should have a vector down the X-axis (i.e. angle 0)
rel_target = (po.target - pod_pos).rotate(-1.23 - Constants.max_turn())
self.assertAlmostEqual(rel_target.y, 0)
self.assertGreater(rel_target.x, 1)
def re_dcat(board: PodBoard, pod: PodState) -> float:
pod_to_check = board.checkpoints[pod.nextCheckId] - pod.pos
# Scaled distance to next check
dist_penalty = pod_to_check.length() / DIST_BASE
# Bonus for each check hit. By making it 2 per check, we ensure that the reward is always
# higher after hitting a check. (If left at 1, the dist_penalty could be slightly greater
# than 1, leading to a DECREASE in reward for hitting a check)
checks_hit = len(board.checkpoints) * pod.laps + pod.nextCheckId
# A tiny bit for the angle. This should really be tiny - its purpose is to serve as a
# tie-breaker (to prevent the pod from going into orbit around a check).
angle = math.fabs(clean_angle(pod_to_check.angle() - pod.angle))
a_penalty = (angle / math.pi) / 10 if angle > Constants.max_turn() else 0
# And finally: this can be important to prevent agents from doing nothing.
# The reduction factor is slightly more than the number of turns it takes
# (on average) to get from one check to another
turn_penalty = pod.turns / 20
return 3 * (checks_hit + 1) \
- dist_penalty \
- a_penalty \
- turn_penalty
def test_get_best_action_works_behind_right(self):
board = PodBoard([Vec2(5000, 5000), Vec2(1000, 1000)])
# Pod is directly right of check, but facing away (slightly to the right)
pod = PodState(Vec2(7000, 5000))
pod.angle = -0.000001
self.__do_get_best_action_assert(board, pod, 0, -Constants.max_turn())
def test_get_best_action_works_right(self):
board = PodBoard([Vec2(5000, 5000), Vec2(1000, 1000)])
# Pod is directly below the check, but the check is behind and to its right
pod = PodState(Vec2(5000, 0))
pod.angle = math.pi * 1.25
self.__do_get_best_action_assert(board, pod, 0, -Constants.max_turn())
def re_dca(board: PodBoard, pod: PodState) -> float:
checks_hit = len(board.checkpoints) * pod.laps + pod.nextCheckId
pod_to_check = board.checkpoints[pod.nextCheckId] - pod.pos
angle = math.fabs(clean_angle(pod_to_check.angle() - pod.angle))
a_penalty = (angle / math.pi) / 10 if angle > Constants.max_turn() else 0
dist_penalty = pod_to_check.length() / DIST_BASE
return 3 * (checks_hit + 1) - dist_penalty - a_penalty
def __init__(self, board: PodBoard):
super().__init__()
# Allow the agent to go beyond the bounds - due to the nature of
# the rounding functions, it's unlikely the agent will ever give
# us the actual min or max
scaled_max_turn = Constants.max_turn() * 1.1
scaled_max_thrust = Constants.max_thrust() + 2 * THRUST_PADDING
angle_spec = array_spec.BoundedArraySpec(
(),
np.float,
minimum=-scaled_max_turn,
maximum=scaled_max_turn)
thrust_spec = array_spec.BoundedArraySpec(
(),
np.int32,
minimum=0,
maximum=scaled_max_thrust)
self._action_spec = {
'angle': angle_spec,
'thrust': thrust_spec
}
angles_spec = array_spec.BoundedArraySpec(
(3,),
np.float,
minimum=-math.pi,
maximum=math.pi)
dist_spec = array_spec.BoundedArraySpec(
(3,),
np.float,
minimum=0,
maximum=Constants.world_x() * 10)
self._observation_spec = {
'angles': angles_spec,
'distances': dist_spec
}
self._time_step_spec = ts.TimeStep(
step_type=array_spec.ArraySpec(shape=(), dtype=np.int32, name='step_type'),
reward=array_spec.ArraySpec(shape=(), dtype=np.float32, name='reward'),
discount=array_spec.ArraySpec(shape=(), dtype=np.float32, name='discount'),
observation=self._observation_spec
)
self._board = board
self._player = Player(AgentController())
self._initial_state = self.get_state()
self._episode_ended = False
def test_actions_produce_all_possible_combinations(self):
# First, collect all unique values
outputs = set()
angles = set()
thrusts = set()
for action in range(0, self.ad.num_actions):
thrust, angle = self.ad.action_to_play(action)
outputs.add((thrust, angle))
angles.add(angle)
thrusts.add(thrust)
# Ensure that we have the correct number of each
self.assertEqual(len(outputs), self.ad.num_actions)
self.assertEqual(len(angles), self.ad.num_angle)
self.assertEqual(len(thrusts), self.ad.num_thrust)
# Ensure that each possibility is present
thrust_inc = Constants.max_thrust() / (self.ad.num_thrust - 1)
for t in range(0, self.ad.num_thrust):
self.assertIn(t * thrust_inc, thrusts)
ang_inc = (Constants.max_turn() * 2) / (self.ad.num_angle - 1)
for a in range(0, self.ad.num_angle):
self.assertIn(a * ang_inc - Constants.max_turn(), angles)
def test_play_to_action_to_play_rounds_angle(self):
action = self.ad.play_to_action(0, Constants.max_turn() * 0.001)
thrust, angle = self.ad.action_to_play(action)
self.assertEqual(thrust, 0)
self.assertEqual(angle, 0)
def test_play_to_action_to_play_both_min(self):
self.__do_p_a_p(0, -Constants.max_turn())
def test_play_to_action_to_play_both_max(self):
self.__do_p_a_p(Constants.max_thrust(), Constants.max_turn())
def test_play_to_action_to_play_max_angle(self):
self.__do_p_a_p(50, Constants.max_turn())
