def optimal_value_strategy(s, action_list):
actions_consequences = []
rotations = []
for action in action_list:
if action.name is "none":
rotation = 0
else:
# optimize action
rotation, _ = heinrich_test(s, action, False, iterations=20)
rotations += [rotation]
# apply optimized rotation
s.pose.rotate(rotation)
actions_consequences.append(
Sim.simulateAction(action, s, num_particles=30))
# restore previous rotation
s.pose.rotate(-rotation)
# Decide best action
selected_action_idx = Sim.decide_minimal(actions_consequences, s)
return selected_action_idx, rotations[selected_action_idx]
def direct_kick_strategy_cool(s, action_list, take_best=False):
actions_consequences = []
rotations = []
fastest_action_dir = None
fastest_action_idx = 0
for idx, action in enumerate(action_list):
if action.name is "none":
rotation = 0
else:
# optimize action
a0 = minimal_rotation(s, action, 1)
a1 = minimal_rotation(s, action, -1)
if np.abs(a0) < np.abs(a1):
rotation = a0
else:
rotation = a1
if np.abs(rotation) > 3:
print(
"WARNING: in direct_kick_strategy_cool no kick found after rotation {0}."
.format(rotation))
rotations += [rotation]
# apply optimized rotation
s.pose.rotate(rotation)
actions_consequences.append(
Sim.simulateAction(action, s, num_particles=30))
# restore previous rotation
s.pose.rotate(-rotation)
if action.name is not "none":
if fastest_action_dir is None or np.abs(rotation) < np.abs(
fastest_action_dir):
fastest_action_dir = rotation
fastest_action_idx = idx
# Decide best action
if take_best:
selected_action_idx = Sim.decide_minimal(actions_consequences, s)
return selected_action_idx, rotations[selected_action_idx]
else:
# print fastest_action_idx, selected_action_idx
return fastest_action_idx, fastest_action_dir
def direct_kick_strategy(s, action_list):
turn_speed = math.radians(5.0)
action_dir = 0
turn_direction = 0
while True:
# turn towards the direction of the action
s.pose.rotate(action_dir)
# Simulate Consequences
actions_consequences = [
Sim.simulateAction(action, s, num_particles=30)
for action in action_list
]
# Decide best action
selected_action_idx = Sim.decide_minimal(actions_consequences, s)
# restore the previous orientation
s.pose.rotate(-action_dir)
if selected_action_idx != 0:
break
elif np.abs(action_dir) > math.pi:
print(
"WARNING: in direct_kick_strategy no kick found after rotation {0}."
.format(action_dir))
break
else:
# decide on rotation direction once
if turn_direction == 0:
attack_direction = attack_dir.get_attack_direction(s)
turn_direction = np.sign(
attack_direction.angle()) # "> 0" => left, "< 0" => right
# set motion request
action_dir += turn_direction * turn_speed
return selected_action_idx, action_dir
def minimal_rotation(s, action, turn_direction):
turn_speed = math.radians(5.0)
action_dir = 0
none = a.Action("none", 0, 0, 0, 0)
none_actions_consequences = Sim.simulateAction(none, s, num_particles=30)
while True:
# turn towards the direction of the action
s.pose.rotate(action_dir)
# Simulate Consequences
actions_consequences = Sim.simulateAction(action, s, num_particles=30)
# Decide best action
selected_action_idx = Sim.decide_minimal(
[none_actions_consequences, actions_consequences], s)
# restore the previous orientation
s.pose.rotate(-action_dir)
if selected_action_idx != 0:
break
elif np.abs(action_dir) > math.pi:
# print("WARNING: in minimal_rotation no kick found after rotation {0}.".format(action_dir))
break
else:
# decide on rotation direction once
if turn_direction == 0:
attack_direction = attack_dir.get_attack_direction(s)
turn_direction = np.sign(
attack_direction.angle()) # "> 0" => left, "< 0" => right
# set motion request
action_dir += turn_direction * turn_speed
return action_dir