def test_pos_within(self):
test_layout = (
""" ##################
#0#. . # . #
#2##### #####1#
# . # . .#3#
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
free = set(pos for pos, val in universe.maze.items() if not val)
self.assertFalse((0, 0) in al)
self.assertRaises(NoPathException, al.pos_within, (0, 0), 0)
self.assertFalse((6, 2) in al)
self.assertRaises(NoPathException, al.pos_within, (6, 2), 0)
self.assertTrue((1, 1) in al)
self.assertEqual(set([(1, 1)]), al.pos_within((1, 1), 0))
target = set([(1, 1), (1, 2), (1,3), (2, 3), (3, 3), (3, 3)])
self.assertEqual(target, al.pos_within((1, 1), 5))
# assuming a_star is working properly
for pos in target:
self.assertTrue(len(al.a_star((1, 1), pos)) < 5)
for pos in free.difference(target):
self.assertTrue(len(al.a_star((1, 1), pos)) >= 5)
def test_bfs_to_self(self):
test_layout = (""" ############
#0. #.1#
############ """)
universe = CTFUniverse.create(test_layout, 2)
al = AdjacencyList(universe.free_positions())
self.assertEqual([], al.bfs((1, 1), [(1, 1), (2, 1)]))
def test_pos_within(self):
test_layout = (""" ##################
#0#. . # . #
#2##### #####1#
# . # . .#3#
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
free = {pos for pos, val in universe.maze.items() if not val}
assert not ((0, 0) in al)
with pytest.raises(NoPathException):
al.pos_within((0, 0), 0)
assert not ((6, 2) in al)
with pytest.raises(NoPathException):
al.pos_within((6, 2), 0)
assert (1, 1) in al
unittest.TestCase().assertCountEqual([(1, 1)], al.pos_within((1, 1),
0))
target = [(1, 1), (1, 2), (1, 3), (2, 3), (3, 3)]
unittest.TestCase().assertCountEqual(target, al.pos_within((1, 1), 5))
# assuming a_star is working properly
for pos in target:
assert len(al.a_star((1, 1), pos)) < 5
for pos in free.difference(target):
assert len(al.a_star((1, 1), pos)) >= 5
def test_bfs_to_self(self):
test_layout = (
""" ############
#0. #.1#
############ """)
universe = CTFUniverse.create(test_layout, 2)
al = AdjacencyList(universe.free_positions())
assert [] == al.bfs((1,1), [(1, 1), (2, 1)])
def test_bfs_to_self(self):
test_layout = (
""" ############
#0. #.1#
############ """)
universe = create_CTFUniverse(test_layout, 2)
al = AdjacencyList(universe)
self.assertEqual([], al.bfs((1,1), [(1, 1), (2, 1)]))
def test_a_star(self):
test_layout = (""" ##################
#0#. . # . #
#2##### #####1#
# . # . .#3#
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
# just a simple smoke test
self.assertEqual(14, len(al.a_star((1, 1), (3, 1))))
def test_path_to_same_position(self):
test_layout = (""" ##################
#0#. . # . #
#2##### #####1#
# . # . .#3#
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
self.assertEqual([], al.a_star((1, 1), (1, 1)))
self.assertEqual([], al.bfs((1, 1), [(1, 1)]))
def test_pos_within(self):
test_layout = (
""" ##################
#0#. . # . #
#2##### #####1#
# . # . .#3#
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
free = {pos for pos, val in universe.maze.items() if not val}
assert not ((0, 0) in al)
with pytest.raises(NoPathException):
al.pos_within((0, 0), 0)
assert not ((6, 2) in al)
with pytest.raises(NoPathException):
al.pos_within((6, 2), 0)
assert (1, 1) in al
unittest.TestCase().assertCountEqual([(1, 1)], al.pos_within((1, 1), 0))
target = [(1, 1), (1, 2), (1,3), (2, 3), (3, 3)]
unittest.TestCase().assertCountEqual(target, al.pos_within((1, 1), 5))
# assuming a_star is working properly
for pos in target:
assert len(al.a_star((1, 1), pos)) < 5
for pos in free.difference(target):
assert len(al.a_star((1, 1), pos)) >= 5
def test_path_to_same_position(self):
test_layout = (
""" ##################
#0#. . # . #
#2##### #####1#
# . # . .#3#
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
assert [] == al.a_star((1, 1), (1, 1))
assert [] == al.bfs((1, 1), [(1, 1)])
def test_a_star(self):
test_layout = (
""" ##################
#0#. . # . #
#2##### #####1#
# . # . .#3#
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
# just a simple smoke test
self.assertEqual(14, len(al.a_star((1, 1), (3, 1))))
def test_extended_adjacency_list(self):
test_layout = (""" ##################
#0#. . # . #
# ##### ##### #
# . # . .#1#
################## """)
universe = CTFUniverse.create(test_layout, 2)
al = AdjacencyList(universe.free_positions())
adjacency_target = {
(7, 3): [(7, 2), (7, 3), (6, 3)],
(1, 3): [(1, 2), (2, 3), (1, 3)],
(12, 1): [(13, 1), (12, 1), (11, 1)],
(16, 2): [(16, 3), (16, 1), (16, 2)],
(15, 1): [(16, 1), (15, 1), (14, 1)],
(5, 1): [(6, 1), (5, 1), (4, 1)],
(10, 3): [(10, 2), (11, 3), (10, 3), (9, 3)],
(7, 2): [(7, 3), (7, 1), (8, 2), (7, 2)],
(1, 2): [(1, 3), (1, 1), (1, 2)],
(3, 3): [(4, 3), (3, 3), (2, 3)],
(13, 3): [(14, 3), (13, 3), (12, 3)],
(8, 1): [(8, 2), (8, 1), (7, 1)],
(16, 3): [(16, 2), (16, 3)],
(6, 3): [(7, 3), (6, 3), (5, 3)],
(14, 1): [(15, 1), (14, 1), (13, 1)],
(11, 1): [(12, 1), (11, 1), (10, 1)],
(4, 1): [(5, 1), (4, 1), (3, 1)],
(1, 1): [(1, 2), (1, 1)],
(12, 3): [(13, 3), (12, 3), (11, 3)],
(8, 2): [(8, 1), (9, 2), (8, 2), (7, 2)],
(7, 1): [(7, 2), (8, 1), (7, 1), (6, 1)],
(9, 3): [(9, 2), (10, 3), (9, 3)],
(2, 3): [(3, 3), (2, 3), (1, 3)],
(10, 1): [(10, 2), (11, 1), (10, 1)],
(5, 3): [(6, 3), (5, 3), (4, 3)],
(13, 1): [(14, 1), (13, 1), (12, 1)],
(9, 2): [(9, 3), (10, 2), (9, 2), (8, 2)],
(6, 1): [(7, 1), (6, 1), (5, 1)],
(3, 1): [(4, 1), (3, 1)],
(11, 3): [(12, 3), (11, 3), (10, 3)],
(16, 1): [(16, 2), (16, 1), (15, 1)],
(4, 3): [(5, 3), (4, 3), (3, 3)],
(14, 3): [(14, 3), (13, 3)],
(10, 2): [(10, 3), (10, 1), (10, 2), (9, 2)]
}
for val in al.values():
val.sort()
for val in adjacency_target.values():
val.sort()
self.assertEqual(adjacency_target, al)
def test_extended_adjacency_list(self):
test_layout = (
""" ##################
#0#. . # . #
# ##### ##### #
# . # . .#1#
################## """)
universe = CTFUniverse.create(test_layout, 2)
al = AdjacencyList(universe.free_positions())
adjacency_target = {(7, 3): [(7, 2), (7, 3), (6, 3)],
(1, 3): [(1, 2), (2, 3), (1, 3)],
(12, 1): [(13, 1), (12, 1), (11, 1)],
(16, 2): [(16, 3), (16, 1), (16, 2)],
(15, 1): [(16, 1), (15, 1), (14, 1)],
(5, 1): [(6, 1), (5, 1), (4, 1)],
(10, 3): [(10, 2), (11, 3), (10, 3), (9, 3)],
(7, 2): [(7, 3), (7, 1), (8, 2), (7, 2)],
(1, 2): [(1, 3), (1, 1), (1, 2)],
(3, 3): [(4, 3), (3, 3), (2, 3)],
(13, 3): [(14, 3), (13, 3), (12, 3)],
(8, 1): [(8, 2), (8, 1), (7, 1)],
(16, 3): [(16, 2), (16, 3)],
(6, 3): [(7, 3), (6, 3), (5, 3)],
(14, 1): [(15, 1), (14, 1), (13, 1)],
(11, 1): [(12, 1), (11, 1), (10, 1)],
(4, 1): [(5, 1), (4, 1), (3, 1)],
(1, 1): [(1, 2), (1, 1)],
(12, 3): [(13, 3), (12, 3), (11, 3)],
(8, 2): [(8, 1), (9, 2), (8, 2), (7, 2)],
(7, 1): [(7, 2), (8, 1), (7, 1), (6, 1)],
(9, 3): [(9, 2), (10, 3), (9, 3)],
(2, 3): [(3, 3), (2, 3), (1, 3)],
(10, 1): [(10, 2), (11, 1), (10, 1)],
(5, 3): [(6, 3), (5, 3), (4, 3)],
(13, 1): [(14, 1), (13, 1), (12, 1)],
(9, 2): [(9, 3), (10, 2), (9, 2), (8, 2)],
(6, 1): [(7, 1), (6, 1), (5, 1)],
(3, 1): [(4, 1), (3, 1)],
(11, 3): [(12, 3), (11, 3), (10, 3)],
(16, 1): [(16, 2), (16, 1), (15, 1)],
(4, 3): [(5, 3), (4, 3), (3, 3)],
(14, 3): [(14, 3), (13, 3)],
(10, 2): [(10, 3), (10, 1), (10, 2), (9, 2)]}
for val in al.values():
val.sort()
for val in adjacency_target.values():
val.sort()
assert adjacency_target == al
def test_a_star(self):
test_layout = (""" ##################
#02.# . # . # #
# ### ####1 #
# ### . # . ##3#
# #
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
#Test distance to middle from both sides
assert 11 == len(al.a_star((1, 1), (7, 2)))
assert 12 == len(al.a_star((2, 1), (7, 2)))
assert 14 == len(al.a_star((16, 1), (7, 2)))
assert 15 == len(al.a_star((15, 1), (7, 2)))
# Test basic assertions
assert 0 == len(al.a_star((1, 1), (1, 1)))
assert 1 == len(al.a_star((1, 1), (2, 1)))
assert 1 == len(al.a_star((2, 1), (1, 1)))
# Test distance to middle from both sides
assert 11 == len(al.a_star((1, 1), (7, 2)))
assert 12 == len(al.a_star((2, 1), (7, 2)))
assert 14 == len(al.a_star((16, 1), (7, 2)))
assert 15 == len(al.a_star((15, 1), (7, 2)))
def test_bfs_exceptions(self):
test_layout = (""" ############
#0. #.1#
############ """)
universe = CTFUniverse.create(test_layout, 2)
al = AdjacencyList(universe.free_positions())
with pytest.raises(NoPathException):
al.bfs((1, 1), [(10, 1)])
with pytest.raises(NoPathException):
al.bfs((1, 1), [(10, 1), (9, 1)])
with pytest.raises(NoPathException):
al.bfs((0, 1), [(10, 1)])
with pytest.raises(NoPathException):
al.bfs((1, 1), [(11, 1)])
def test_a_star2(self):
test_layout = (
""" ########
#1# #
# # #0 #
# #
######## """ )
universe = CTFUniverse.create(test_layout, 2)
al = AdjacencyList(universe.free_positions())
#Test distance to middle from both sides
print(al.a_star(universe.bots[0].current_pos, universe.bots[1].current_pos))
print(al.a_star(universe.bots[1].current_pos, universe.bots[0].current_pos))
assert 7 == len(al.a_star(universe.bots[0].current_pos, universe.bots[1].current_pos))
assert 7 == len(al.a_star(universe.bots[1].current_pos, universe.bots[0].current_pos))
def set_initial(self):
# Now ``self.current_uni`` and ``self.current_state`` are known.
# ``set_initial`` is always called before ``get_move``, so we can do some
# additional initialisation here
# Initialize an AdjacencyList for all reachable positions
# This will help us find shortest paths
# see in graph.py for more details
self.adjacency = AdjacencyList(
self.current_uni.reachable([self.initial_pos]))
# Once we have picked a food item to go to, we’ll note it here
# Otherwise we risk flapping between two states
self.next_food = None
def test_a_star(self):
test_layout = (
""" ##################
#02.# . # . # #
# ### ####1 #
# ### . # . ##3#
# #
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
#Test distance to middle from both sides
assert 11 == len(al.a_star((1, 1), (7, 2)))
assert 12 == len(al.a_star((2, 1), (7, 2)))
assert 14 == len(al.a_star((16, 1), (7, 2)))
assert 15 == len(al.a_star((15, 1), (7, 2)))
class SmartEatingPlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def get_move(self):
# check, if food is still present
if (self.next_food is None
or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.next_food = self.rnd.choice(self.enemy_food)
try:
dangerous_enemy_pos = [bot.current_pos
for bot in self.enemy_bots if bot.is_destroyer]
next_pos = self.goto_pos(self.next_food)
# check, if the next_pos has an enemy on it
if next_pos in dangerous_enemy_pos:
# whoops, better wait this round and take another food next time
self.next_food = None
return datamodel.stop
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
return datamodel.stop
def attack_move(self):
self.adjacency = AdjacencyList(self.current_uni.free_positions())
attackpath = []
if self.tracking_idx is not None:
# if the enemy is no longer in our zone
if not self.team.in_zone(self.tracking_target.current_pos):
self.tracking_idx = None
return self.go_for_food()
# otherwise update the path to the target
else:
attackpath = self.path_to_target
if self.tracking_idx is None:
# check the enemy positions
possible_targets = [enemy for enemy in self.enemy_bots
if self.team.in_zone(enemy.current_pos)]
if possible_targets:
# get the path to the closest one
try:
possible_paths = [(enemy,
self.adjacency.a_star(self.current_pos, enemy.current_pos))
for enemy in possible_targets]
except NoPathException:
return None
else:
return None
if possible_paths:
closest_enemy, path = min(possible_paths,
key=lambda enemy_path: len(enemy_path[1]))
self.tracking_idx = closest_enemy.index
if len(attackpath)==0:
return self.random_move()
if len(attackpath)>0 and self.round_index%20==0:
return self.random_move()
return diff_pos(self.current_pos, attackpath.pop())
def set_initial(self):
'''Sets the initial values.
'''
self.adjacency = AdjacencyList(
self.current_uni.reachable([self.initial_pos]))
self.memory.store((self._index, 'roles'), self.roles)
self.memory.store((self._index, 'sit'), self.sit)
class SmartEatingPlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(
self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def get_move(self):
# check, if food is still present
if (self.next_food is None or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.next_food = self.rnd.choice(self.enemy_food)
try:
dangerous_enemy_pos = [
bot.current_pos for bot in self.enemy_bots if bot.is_destroyer
]
next_pos = self.goto_pos(self.next_food)
# check, if the next_pos has an enemy on it
if next_pos in dangerous_enemy_pos:
# whoops, better wait this round and take another food next time
self.next_food = None
return datamodel.stop
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
return datamodel.stop
class BorderPlayer(AbstractPlayer):
""" A player that makes moves at random. """
def find_path(self, thingslist):
""" finds the path to the nearest object
*thingslist* - list of tuples with objects positions
"""
self.adjacency = AdjacencyList(self.current_uni.free_positions())
try:
pathd = self.adjacency.bfs(self.current_pos, thingslist)
except NoPathException:
return None
return pathd
def read_score(self):
self.score_history[0, self.round_index] = self.current_uni.teams[0].score
self.score_history[1, self.round_index] = self.current_uni.teams[1].score
def get_move(self):
border_path = self.find_path(self.team_border)
self.say("Border!!!!")
if len(border_path)==0:
return stop
if border_path==None:
return stop
return diff_pos(self.current_pos, border_path.pop())
def path_to_target(self):
""" Path to the target we are currently tracking. """
self.adjacency = AdjacencyList(self.current_uni.free_positions())
try:
return self.adjacency.a_star(self.current_pos,
self.tracking_target.current_pos)
except NoPathException:
return None
def set_initial(self):
# Now ``self.current_uni`` and ``self.current_state`` are known.
# ``set_initial`` is always called before ``get_move``, so we can do some
# additional initialization here
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
# Just printing the universe to give you an idea, please remove all
# print statements in the final player.
print self.current_uni.pretty
def test_a_star_exceptions(self):
test_layout = (""" ############
#0. #.1#
############ """)
universe = CTFUniverse.create(test_layout, 2)
al = AdjacencyList(universe.free_positions())
self.assertRaises(NoPathException, al.a_star, (1, 1), (10, 1))
self.assertRaises(NoPathException, al.a_star, (0, 1), (10, 1))
self.assertRaises(NoPathException, al.a_star, (1, 1), (11, 1))
def test_bfs_exceptions(self):
test_layout = (
""" ############
#0. #.1#
############ """)
universe = CTFUniverse.create(test_layout, 2)
al = AdjacencyList(universe.free_positions())
with pytest.raises(NoPathException):
al.bfs((1, 1), [(10, 1)])
with pytest.raises(NoPathException):
al.bfs((1, 1), [(10, 1), (9, 1)])
with pytest.raises(NoPathException):
al.bfs((0, 1), [(10, 1)])
with pytest.raises(NoPathException):
al.bfs((1, 1), [(11, 1)])
def test_basic_adjacency_list(self):
test_layout = (
""" ######
# #
###### """)
universe = CTFUniverse.create(test_layout, 0)
al = AdjacencyList(universe.free_positions())
target = { (4, 1): [(4, 1), (3, 1)],
(1, 1): [(2, 1), (1, 1)],
(2, 1): [(3, 1), (2, 1), (1, 1)],
(3, 1): [(4, 1), (3, 1), (2, 1)]}
for val in al.values():
val.sort()
for val in target.values():
val.sort()
assert target == al
def find_path(self, thingslist):
""" finds the path to the nearest object
*thingslist* - list of tuples with objects positions
"""
self.adjacency = AdjacencyList(self.current_uni.free_positions())
try:
pathd = self.adjacency.bfs(self.current_pos, thingslist)
except NoPathException:
return None
return pathd
def test_basic_adjacency_list(self):
test_layout = (""" ######
# #
###### """)
universe = CTFUniverse.create(test_layout, 0)
al = AdjacencyList(universe.free_positions())
target = {
(4, 1): [(4, 1), (3, 1)],
(1, 1): [(2, 1), (1, 1)],
(2, 1): [(3, 1), (2, 1), (1, 1)],
(3, 1): [(4, 1), (3, 1), (2, 1)]
}
for val in al.values():
val.sort()
for val in target.values():
val.sort()
self.assertEqual(target, al)
def test_pos_within(self):
test_layout = (
""" ##################
#0#. . # . #
#2##### #####1#
# . # . .#3#
################## """)
universe = CTFUniverse.create(test_layout, 4)
al = AdjacencyList(universe.free_positions())
free = set(universe.maze.pos_of(Free))
self.assertFalse((0, 0) in al)
self.assertRaises(NoPathException, al.pos_within, (0, 0), 0)
self.assertFalse((6, 2) in al)
self.assertRaises(NoPathException, al.pos_within, (6, 2), 0)
self.assertTrue((1, 1) in al)
self.assertEqual(set([(1, 1)]), al.pos_within((1, 1), 0))
target = set([(1, 1), (1, 2), (1,3), (2, 3), (3, 3), (3, 3)])
self.assertEqual(target, al.pos_within((1, 1), 5))
# assuming a_star is working properly
for pos in target:
self.assertTrue(len(al.a_star((1, 1), pos)) < 5)
for pos in free.difference(target):
self.assertTrue(len(al.a_star((1, 1), pos)) >= 5)
def set_initial(self):
# Now ``self.current_uni`` and ``self.current_state`` are known.
# ``set_initial`` is always called before ``get_move``, so we can do some
# additional initialisation here
# Initialize an AdjacencyList for all reachable positions
# This will help us find shortest paths
# see in graph.py for more details
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
# Once we have picked a food item to go to, we’ll note it here
# Otherwise we risk flapping between two states
self.next_food = None
def test_a_star3(self):
test_layout = ("""
################################################################
#0# # # # # # #
# ######### ###### # # ### #
# # # ######## ## ## # # # # # #
# ############ # # # # # ## ############### #
# # # ### # # # ### ## # #### ###
# ####### #### # # # # # # #
# # 1 # ### ##### ## ############# ###########
# # # # # # # # ## # # #
# ######################### ## ## ######### ##############"""
)
universe = CTFUniverse.create(test_layout, 2)
al = AdjacencyList(universe.free_positions())
#Test distance to middle from both sides
assert 15 == len(
al.a_star(universe.bots[0].current_pos,
universe.bots[1].current_pos))
assert 15 == len(
al.a_star(universe.bots[1].current_pos,
universe.bots[0].current_pos))
def test_basic_adjacency_list(self):
test_layout = (""" ######
# #
###### """)
universe = CTFUniverse.create(test_layout, 0)
al = AdjacencyList(universe.free_positions())
target = {
(4, 1): [(4, 1), (3, 1)],
(1, 1): [(2, 1), (1, 1)],
(2, 1): [(3, 1), (2, 1), (1, 1)],
(3, 1): [(4, 1), (3, 1), (2, 1)]
}
self.assertDictEqual(target, al)
def test_pos_within(self):
test_layout = (
""" ##################
#0#. . # . #
#2##### #####1#
# . # . .#3#
################## """)
universe = create_CTFUniverse(test_layout, 4)
al = AdjacencyList(universe)
free = set(universe.maze.pos_of(Free))
self.assertRaises(NoPositionException, al.pos_within, (0, 0), 0)
self.assertRaises(NoPositionException, al.pos_within, (6, 2), 0)
self.assertEqual(set([(1, 1)]), al.pos_within((1, 1), 0))
target = set([(1, 1), (1, 2), (1,3), (2, 3), (3, 3), (3, 3)])
self.assertEqual(target, al.pos_within((1, 1), 5))
# assuming a_star is working properly
for pos in target:
self.assertTrue(len(al.a_star((1, 1), pos)) < 5)
for pos in free.difference(target):
self.assertTrue(len(al.a_star((1, 1), pos)) >= 5)
class OurPlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def get_move(self):
# from SmartRandom
dangerous_enemy_pos = [bot.current_pos
for bot in self.enemy_bots if bot.is_destroyer]
killable_enemy_pos = [bot.current_pos
for bot in self.enemy_bots if bot.is_harvester]
# easy kill (please test)
for killable in killable_enemy_pos:
if killable in self.legal_moves.items():
move = diff_pos(self.current_pos, killable)
self.say("Easy kill!")
return move
#pdb.set_trace()
# panic
# for dangerous in dangerous_enemy_pos:
# if killable in self.legal_moves.items():
# move = diff_pos(self.current_pos, killable)
# self.say("Easy kill!")
# return move
# check, if food is still present
# if the nearest is not suitable, choose one at random!
if (self.next_food is None
or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
# SUBOPTIMAL (chooses at random)
self.next_food = self.rnd.choice(self.enemy_food)
try:
next_pos = self.goto_pos(self.next_food)
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
return datamodel.stop
class BFSPlayer(AbstractPlayer):
""" This player uses breadth first search to always go to the closest food.
This player uses an adjacency list [1] to store the topology of the
maze. It will then do a breadth first search [2] to search for the
closest food. When found, it will follow the determined path until it
reaches the food. This continues until all food has been eaten or the
enemy wins.
The adjacency lits representation (`AdjacencyList`) and breadth first search
(`AdjacencyList.bfs`) are imported from `pelita.graph`.
* [1] http://en.wikipedia.org/wiki/Adjacency_list
* [2] http://en.wikipedia.org/wiki/Breadth-first_search
"""
def set_initial(self):
# Before the game starts we initialise our adjacency list.
self.adjacency = AdjacencyList(self.current_uni)
self.current_path = self.bfs_food()
def bfs_food(self):
""" Breadth first search for food.
Returns
-------
path : a list of tuples (int, int)
The positions (x, y) in the path from the current position to the
closest food. The first element is the final destination.
"""
try:
return self.adjacency.bfs(self.current_pos, self.enemy_food)
except NoPathException:
return [self.current_pos]
def get_move(self):
if self.current_pos == self.initial_pos:
# we have probably been killed
# reset the path
self.current_path = None
if not self.current_path:
self.current_path = self.bfs_food()
new_pos = self.current_path.pop()
return diff_pos(self.current_pos, new_pos)
class FoodEatingPlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def get_move(self):
# check, if food is still present
if self.next_food is None or self.next_food not in self.enemy_food:
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.next_food = self.rnd.choice(self.enemy_food)
try:
next_pos = self.goto_pos(self.next_food)
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
return datamodel.stop
class FoodEatingPlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(
self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def get_move(self):
# check, if food is still present
if (self.next_food is None or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.next_food = self.rnd.choice(self.enemy_food)
try:
next_pos = self.goto_pos(self.next_food)
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
return datamodel.stop
def len_of_shortest_path(layout):
uni = CTFUniverse.create(layout, 2)
al = AdjacencyList(uni.free_positions())
path = al.a_star(uni.bots[0].current_pos, uni.bots[1].current_pos)
return len(path)
class ItalianPossessivePlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
#print(len(self.adjacency.a_star(self.current_pos, pos)))
if len(self.adjacency.a_star(self.current_pos, pos)) > 0:
return self.adjacency.a_star(self.current_pos, pos)[-1]
else:
return self.current_pos
# Determine the closest enemy
def get_enemy_to_block(self):
dist_enemy0 = len(self.adjacency.a_star(self.current_pos, self.enemy_bots[0].current_pos))
dist_enemy1 = len(self.adjacency.a_star(self.current_pos, self.enemy_bots[1].current_pos))
self.enemy_to_block = np.argmin([dist_enemy0, dist_enemy1])
# Determine closest food for that enemy
def get_closest_food(self):
self.enemy_next_food_distance_list = np.array(list(map(
lambda x: len(self.adjacency.a_star(self.enemy_bots[self.enemy_to_block].current_pos, x)),
self.team_food)))
# Decide which food to protect
def get_food_to_protect(self):
self.minimum_index = np.argmin(self.enemy_next_food_distance_list)
self.food_to_protect = self.team_food[self.minimum_index]
# Enemy path
def get_enemy_path(self):
self.enemy_path = self.adjacency.a_star(self.enemy_bots[self.enemy_to_block].current_pos, self.food_to_protect)
# Point to intercept
def get_point_to_intercept(self):
if len(self.enemy_path) > 0:
index = np.round(len(self.enemy_path)/2)
self.p_intercept = self.enemy_path[index.astype(int)]
else:
while len(self.enemy_path) == 0:
self.enemy_next_food_distance_list = np.delete(self.enemy_next_food_distance_list, self.minimum_index)
self.get_food_to_protect()
self.get_enemy_path()
index = np.round(len(self.enemy_path)/2)
self.p_intercept = self.enemy_path[index.astype(int)]
#def sit_on_food(self):
def get_move(self):
# check, if food is still present
self.say("Go Away!!")
if (self.next_food is None
or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.get_enemy_to_block()
self.get_closest_food()
self.get_food_to_protect()
self.get_enemy_path()
self.get_point_to_intercept()
try:
next_pos = self.goto_pos(self.p_intercept)
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
print("Help!")
return datamodel.stop
class ExtremelyHungryPlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def compute_food_score(self, distance_decay=2.5):
""" Compute: distance to every pill, first step for every pill.
Out: dict of step options weighed by distance """
# initialise a dict of step options: {next_cell: weight_count}
# loop through the list of available pills
for p in self.enemy_food:
# compute the path to the next one
path_to_pill = self.adjacency.a_star(self.current_pos, p)
first_step = diff_pos(self.current_pos, path_to_pill[-1])
# compute the length for scaling
weight = np.exp(-len(path_to_pill) / distance_decay)
# populate the step options dict
self.step_options[first_step] += weight
def compute_enemy_score(self, enemy_distance_decay=1.5):
"""Update step_options to avoid the enemy. Currently only resets a
valus of the step_options to -1 if it means taking the shortest path to
the enemy.
"""
decay_per_distance = lambda d: -2 * np.exp(-d ** 2 / enemy_distance_decay ** 2)
self.repulse_bot(self.enemy_bots, decay_per_distance)
def repulse_bot(self, bot_list, function):
"""In: list of bots to repulse, repulsion function
"""
# get the possible positions
for lm in self.legal_moves:
# The position we would be in in this case "possible_position"
p_pos = add_pos(self.current_pos, lm)
for e in bot_list:
# compute the path to the next move
try:
if e.noisy:
continue
except AttributeError:
pass
try:
path_to_bot = self.adjacency.a_star(p_pos, e.current_pos)
except pelita.graph.NoPathException:
path_to_bot = []
distance = len(path_to_bot)
self.step_options[lm] += function(distance)
def compute_friend_score(self):
"""Based on the friend_bot, decay function """
d_decay = 5
max_repulsion = 1.1 * self.step_options[max(self.step_options)]
decay_function = lambda d: -max_repulsion * np.exp(-d ** 2 / d_decay ** 2)
self.repulse_bot(self.other_team_bots, decay_function)
def compute_optimal_move(self):
""" Compute the optimal move based on the coordinate with the highest
score """
# recommend the step with the highest score
recommended_step = max(self.step_options, key=self.step_options.get)
self.move = recommended_step
# self.move = diff_pos(self.current_pos, recommended_coordinate)
def get_move(self):
# check, if food is still present
self.say("ID %d" % self.me.index)
if self.next_food is None or self.next_food not in self.enemy_food:
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.next_food = self.rnd.choice(self.enemy_food)
self.step_options = defaultdict(float)
# initialize the step options to zero for all valid moves.
# This is important, otherwise the bot will not consider all options
for lm in self.legal_moves:
self.step_options[lm] = 0
self.compute_food_score()
self.compute_enemy_score()
self.compute_friend_score()
self.compute_optimal_move()
if self.me.index == 0:
self.print_scores()
# import pdb; pdb.set_trace()
try:
return self.move
except NoPathException:
print("Help!")
return datamodel.stop
def print_scores(self):
""" Print the energy landscape for the next part """
# compute the indices
x, y = self.current_pos
l_coor = self.step_options[(-1, 0)]
r_coor = self.step_options[(1, 0)]
u_coor = self.step_options[(0, -1)]
d_coor = self.step_options[(0, 1)]
c_coor = self.step_options[(0, 0)]
# scores = map(self.step_options.get, (u_idx, l_idx, r_idx, d_idx))
print_str = """
%3.2f
%3.2f %3.2f %3.2f
%3.2f""" % (
u_coor,
l_coor,
c_coor,
r_coor,
d_coor,
)
print(print_str)
print("Direction: ", self.move)
class ExtremelyHungryPlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def compute_food_score(self, distance_decay=2.5):
""" Compute: distance to every pill, first step for every pill.
Out: dict of step options weighed by distance """
#initialise a dict of step options: {next_cell: weight_count}
# loop through the list of available pills
for p in self.enemy_food:
# compute the path to the next one
path_to_pill = self.adjacency.a_star(self.current_pos, p)
first_step = diff_pos(self.current_pos, path_to_pill[-1])
# compute the length for scaling
weight = np.exp(-len(path_to_pill)/distance_decay)
# populate the step options dict
self.step_options[first_step]+=weight
def compute_enemy_score(self, enemy_distance_decay=1.5):
"""Update step_options to avoid the enemy. Currently only resets a
valus of the step_options to -1 if it means taking the shortest path to
the enemy.
"""
decay_per_distance = lambda d: \
-2*np.exp(-d**2/enemy_distance_decay**2)
self.repulse_bot(self.enemy_bots, decay_per_distance)
def repulse_bot(self, bot_list, function):
'''In: list of bots to repulse, repulsion function
'''
# get the possible positions
for lm in self.legal_moves:
# The position we would be in in this case "possible_position"
p_pos = add_pos(self.current_pos, lm)
for e in bot_list:
# compute the path to the next move
try:
if e.noisy:
continue
except AttributeError:
pass
try:
path_to_bot = self.adjacency.a_star(p_pos, e.current_pos)
except pelita.graph.NoPathException:
path_to_bot = []
distance = len(path_to_bot)
self.step_options[lm] += function(distance)
def compute_friend_score(self):
'''Based on the friend_bot, decay function '''
d_decay = 5
max_repulsion = 1.1*self.step_options[max(self.step_options)]
decay_function= lambda d: -max_repulsion*np.exp(-d**2 / d_decay**2)
self.repulse_bot(self.other_team_bots, decay_function)
def compute_optimal_move(self):
""" Compute the optimal move based on the coordinate with the highest
score """
# recommend the step with the highest score
recommended_step = max(self.step_options, key=self.step_options.get)
self.move = recommended_step
#self.move = diff_pos(self.current_pos, recommended_coordinate)
def get_move(self):
# check, if food is still present
self.say("ID %d" % self.me.index)
if (self.next_food is None
or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.next_food = self.rnd.choice(self.enemy_food)
self.step_options = defaultdict(float)
# initialize the step options to zero for all valid moves.
# This is important, otherwise the bot will not consider all options
for lm in self.legal_moves:
self.step_options[lm] = 0
self.compute_food_score()
self.compute_enemy_score()
self.compute_friend_score()
self.compute_optimal_move()
if self.me.index == 0:
self.print_scores()
#import pdb; pdb.set_trace()
try:
return self.move
except NoPathException:
print("Help!")
return datamodel.stop
def print_scores(self):
""" Print the energy landscape for the next part """
# compute the indices
x,y = self.current_pos
l_coor = self.step_options[(-1,0)]
r_coor = self.step_options[(1,0)]
u_coor = self.step_options[(0,-1)]
d_coor = self.step_options[(0,1)]
c_coor = self.step_options[(0,0)]
# scores = map(self.step_options.get, (u_idx, l_idx, r_idx, d_idx))
print_str = """
%3.2f
%3.2f %3.2f %3.2f
%3.2f""" % (u_coor, l_coor, c_coor, r_coor, d_coor)
print(print_str)
print("Direction: ", self.move)
def set_initial(self):
self.adjacency = AdjacencyList(
self.current_uni.reachable([self.initial_pos]))
self.tolerance = 5
self.range = range(7, 15)
class EatingPlayerLower(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(
self.current_uni.reachable([self.initial_pos]))
self.tolerance = 5
self.range = range(1, 9)
def gotoPos(self, aim):
return (self.adjacency.a_star(self.current_pos, aim))[-1]
def choose_strategy(self):
if self.me.is_harvester:
minDist2Bot = self.chooseClosestEnemy()
strategy = 'run' if minDist2Bot[0] <= self.tolerance else 'eat'
else:
strategy = 'eat'
return (strategy)
def chooseClosestEnemy(self):
dist2bot = np.array([
len(self.adjacency.a_star(self.current_pos, j.current_pos))
if j.is_destroyer else 1000 for j in self.enemy_bots
])
minDist = dist2bot.min()
minPos = [x.current_pos for x in self.enemy_bots][dist2bot.argmin()]
return (minDist, minPos)
def eatMaxSafety(self, enemy_pos):
next_step = np.zeros(len(self.enemy_food))
for i, j in enumerate(self.enemy_food):
next_step[i] = len(
self.adjacency.a_star(
self.adjacency.a_star(self.current_pos, j)[-1], enemy_pos))
max_safe = next_step.max()
max_pos = self.enemy_food[next_step.argmax()]
return (max_safe, max_pos)
def get_move(self):
strategy = self.choose_strategy()
nextPos = self.chooseNextPos(strategy)
move = diffPos(self.current_pos, nextPos)
if not move in self.legal_moves.keys():
move = self.rnd.choice(list(self.legal_moves.keys()))
return (move)
def chooseClosestFood(self):
dist2food = np.array([
len(self.adjacency.a_star(self.current_pos,
(x, y))) if y in self.range else 1000
for x, y in self.enemy_food
])
minDist = dist2food.min()
minPos = self.enemy_food[dist2food.argmin()]
return (minDist, minPos)
def chooseNextPos(self, strategy):
if strategy == 'eat':
minFoodDist, posMinFood = self.chooseClosestFood()
aim = posMinFood
nextPos = self.gotoPos(aim)
elif strategy == 'run':
dummy, aim = self.chooseClosestEnemy()
dummy2, aim2 = self.eatMaxSafety(aim)
nextPos = self.gotoPos(aim2)
return (nextPos)
class JakovPlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def get_move(self):
# check, if food is still present
if (self.next_food is None
or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.next_food = self.rnd.choice(self.enemy_food) # !!! to improve: not random from all enemy_food by from closest food
# determine enemy positions dangerous & killable
dangerous_enemy_pos = [bot.current_pos
for bot in self.enemy_bots if bot.is_destroyer]
non_noisy_dangerous_enemy_pos = [bot.current_pos
for bot in self.enemy_bots if (bot.is_destroyer and not bot.noisy)]
#
killable_enemy_pos = [bot.current_pos
for bot in self.enemy_bots if bot.is_harvester]
try:
next_pos = self.goto_pos(self.next_food)
# next_pos = self.rnd.choice([(0,1),(0,-1),(1,0),(-1,0)])
move = diff_pos(self.current_pos, next_pos)
my_adjecent_pos = self.adjacency.pos_within(self.current_pos,5)
legal_moves = self.legal_moves
# check if the next position is dangerous
# list of dangerous enemy adecent positions
# dangerous_enemy_adj_pos = []
acceptable_adjecent_pos = list(my_adjecent_pos)
# for position in dangerous_enemy_pos:
for position in non_noisy_dangerous_enemy_pos:
dangerous_enemy_adj_pos = self.adjacency.pos_within(position,3)
for enemy_adj_pos in dangerous_enemy_adj_pos:
if enemy_adj_pos in acceptable_adjecent_pos:
acceptable_adjecent_pos.remove(enemy_adj_pos)
# TODO: improve to -> escape to the direction oposite from the enemy
if len(acceptable_adjecent_pos) == 0:
return self.rnd.choice(list(legal_moves.keys()))
if next_pos not in my_adjecent_pos:
next_pos = self.rnd.choice(list(my_adjecent_pos))
move = diff_pos(self.current_pos, next_pos)
# Remove stop
# try:
# del legal_moves[datamodel.stop]
# except KeyError:
# pass
# # now remove the move that would lead to the enemy
# # unless there is no where else to go.
# if len(legal_moves) > 1:
# for (k,v) in legal_moves.items():
# if v in dangerous_enemy_pos:
# break
# del legal_moves[k]
# # just in case, there is really no way to go to:
# if not legal_moves:
# return datamodel.stop
# # and select a move at random
# return self.rnd.choice(list(legal_moves.keys()))
# selecting one of the moves
# while next_pos in dangerous_enemy_pos:
# move = self.rnd.choice(possible_moves)
# next_pos = (self.current_pos[0] + move[0],self.current_pos[1] + move[1])
self.say("bla bla!")
return move
except NoPathException:
return datamodel.stop
class ItalianPossessivePlayer(AbstractPlayer):
def set_initial(self):
self.adjacency = AdjacencyList(
self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
#print(len(self.adjacency.a_star(self.current_pos, pos)))
if len(self.adjacency.a_star(self.current_pos, pos)) > 0:
return self.adjacency.a_star(self.current_pos, pos)[-1]
else:
return self.current_pos
# Determine the closest enemy
def get_enemy_to_block(self):
dist_enemy0 = len(
self.adjacency.a_star(self.current_pos,
self.enemy_bots[0].current_pos))
dist_enemy1 = len(
self.adjacency.a_star(self.current_pos,
self.enemy_bots[1].current_pos))
self.enemy_to_block = np.argmin([dist_enemy0, dist_enemy1])
# Determine closest food for that enemy
def get_closest_food(self):
self.enemy_next_food_distance_list = np.array(
list(
map(
lambda x: len(
self.adjacency.a_star(
self.enemy_bots[self.enemy_to_block].current_pos, x
)), self.team_food)))
# Decide which food to protect
def get_food_to_protect(self):
self.minimum_index = np.argmin(self.enemy_next_food_distance_list)
self.food_to_protect = self.team_food[self.minimum_index]
# Enemy path
def get_enemy_path(self):
self.enemy_path = self.adjacency.a_star(
self.enemy_bots[self.enemy_to_block].current_pos,
self.food_to_protect)
# Point to intercept
def get_point_to_intercept(self):
if len(self.enemy_path) > 0:
index = np.round(len(self.enemy_path) / 2)
self.p_intercept = self.enemy_path[index.astype(int)]
else:
while len(self.enemy_path) == 0:
self.enemy_next_food_distance_list = np.delete(
self.enemy_next_food_distance_list, self.minimum_index)
self.get_food_to_protect()
self.get_enemy_path()
index = np.round(len(self.enemy_path) / 2)
self.p_intercept = self.enemy_path[index.astype(int)]
#def sit_on_food(self):
def get_move(self):
# check, if food is still present
self.say("Go Away!!")
if (self.next_food is None or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.get_enemy_to_block()
self.get_closest_food()
self.get_food_to_protect()
self.get_enemy_path()
self.get_point_to_intercept()
try:
next_pos = self.goto_pos(self.p_intercept)
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
print("Help!")
return datamodel.stop
class OurPlayer(BasePlayer):
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def abs_pos(self, vector):
return (self.current_pos[0]+vector[0], self.current_pos[1]+vector[1])
def get_move(self):
# from SmartRandom
dangerous_enemy_pos = [bot.current_pos
for bot in self.enemy_bots if (bot.is_destroyer and not bot.noisy)]
killable_enemy_pos = [bot.current_pos
for bot in self.enemy_bots if (bot.is_harvester and not bot.noisy)]
# easy kill (kind of tested)
for killable in killable_enemy_pos:
if killable in self.legal_moves.values():
self.say("Easy kill!")
print("Easy kill!")
move = diff_pos(self.current_pos, killable)
return move
# don't die
forbidden_moves = []
for dangerous in dangerous_enemy_pos:
relative_pos = diff_pos(self.current_pos, dangerous)
# check if the destroyer is nearby
if relative_pos in ( (0,1), (1,0), (-1,0), (0,-1)):
self.say("Enemy nearby!")
forbidden_moves.append(relative_pos)
if relative_pos in ( (0,2), (2,0), (-2,0), (0,-2)):
self.say("Enemy in sight!")
rpx, rpy = relative_pos
forbidden_moves.append( (rpx//2, rpy//2) )
if relative_pos in ( (1,1), (1,-1), (-1,1), (-1,-1)):
self.say("Enemy on diagonal!")
rpx, rpy = relative_pos
forbidden_moves.append( (0, rpy) )
forbidden_moves.append( (rpx, 0) )
forbidden_absolute_positions = [self.abs_pos(fm) for fm in forbidden_moves]
# forbidden_absolute_positions WAS tested (kind of)
# it doesn't account for walls (relevant in the second case)
# check, if food is still present
# if the nearest is not suitable, choose one at random!
if (self.next_food is None
or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.next_food = self.get_efficient_eater_move()
try:
move = self.get_efficient_eater_move()
# if it's not allowed, take a random move
if move in forbidden_moves:
# but we are not checking if it's forbidden again!
next_pos = self.rnd.choice(list(self.legal_moves.values()))
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
return datamodel.stop
class DrunkPlayer(AbstractPlayer):
""" Basically a clone of the RandomPlayer. """
def __init__(self):
# Do some basic initialisation here. You may also accept additional
# parameters which you can specify in your factory.
# Note that any other game variables have not been set yet. So there is
# no ``self.current_uni`` or ``self.current_state``
self.sleep_rounds = 0
def set_initial(self):
# Now ``self.current_uni`` and ``self.current_state`` are known.
# ``set_initial`` is always called before ``get_move``, so we can do some
# additional initialisation here
# Just printing the universe to give you an idea, please remove all
# print statements in the final player.
print self.current_uni.pretty
def check_pause(self):
# make a pause every fourth step because whatever :)
if self.sleep_rounds <= 0:
if self.rnd.random() > 0.75:
self.sleep_rounds = 3
if self.sleep_rounds > 0:
self.sleep_rounds -= 1
self.say("I am confused. Very confused.")
return stop
def get_move(self):
utility_function()
for i in range(10):
print self.enemy_bots[0].current_pos
print " "
self.check_pause()
# legal_moves returns a dict {move: position}
# we always need to return a move
possible_moves = self.legal_moves.keys()
# selecting one of the moves
return self.rnd.choice(possible_moves)
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def goto_pos(self, pos):
return self.adjacency.a_star(self.current_pos, pos)[-1]
def get_move(self):
# check, if food is still present
if (self.next_food is None
or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
return datamodel.stop
self.next_food = self.rnd.choice(self.enemy_food)
try:
next_pos = self.goto_pos(self.next_food)
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
return datamodel.stop
class HungryPlayer(AbstractPlayer):
""" Basically a clone of the FoodEatingPlayer. """
def __init__(self):
# Do some basic initialisation here. You may also accept additional
# parameters which you can specify in your factory.
# Note that any other game variables have not been set yet. So there is
# no ``self.current_uni`` or ``self.current_state``
pass
def set_initial(self):
# Now ``self.current_uni`` and ``self.current_state`` are known.
# ``set_initial`` is always called before ``get_move``, so we can do some
# additional initialisation here
# Initialize an AdjacencyList for all reachable positions
# This will help us find shortest paths
# see in graph.py for more details
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
# Once we have picked a food item to go to, we’ll note it here
# Otherwise we risk flapping between two states
self.next_food = None
def path_to(self, pos):
""" Given a position, this return a shortest path from the current position. """
return self.adjacency.a_star(self.current_pos, pos)
def get_move(self):
# check, if food is still present, otherwise go somewhere else
if (self.next_food is None
or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
self.say("I am hungry.")
return stop
# all the food is in self.enemy_food
# we just pick one to go to
# (of course, there may be a smarter choice than just going random)
self.next_food = self.rnd.choice(self.enemy_food)
try:
# figure out the path to take
shortest_path = self.path_to(self.next_food)
# our next position is the last element in the path
next_pos = shortest_path[-1]
# we are a little exited about eating
# (this does not account for any food we additionally eat on our way
# to the food we have picked.)
if len(shortest_path) == 1:
self.say("Yay. Food next.")
else:
self.say("Eating in {0} steps.".format(len(shortest_path)))
# should we check for the enemy at this position?
# self.enemy_bots ?
# Naah – we risk it :)
# the difference between here and there
# is the direction we need to go to
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
# whoops, there is no path possible
# we better wait
return stop
def set_initial(self):
self.adjacency = AdjacencyList(
self.current_uni.reachable([self.initial_pos]))
self.next_food = None
class HungryPlayer(AbstractPlayer):
""" Basically a clone of the FoodEatingPlayer. """
def __init__(self):
# Do some basic initialisation here. You may also accept additional
# parameters which you can specify in your factory.
# Note that any other game variables have not been set yet. So there is
# no ``self.current_uni`` or ``self.current_state``
pass
def set_initial(self):
# Now ``self.current_uni`` and ``self.current_state`` are known.
# ``set_initial`` is always called before ``get_move``, so we can do some
# additional initialisation here
# Initialize an AdjacencyList for all reachable positions
# This will help us find shortest paths
# see in graph.py for more details
self.adjacency = AdjacencyList(
self.current_uni.reachable([self.initial_pos]))
# Once we have picked a food item to go to, we’ll note it here
# Otherwise we risk flapping between two states
self.next_food = None
def path_to(self, pos):
""" Given a position, this return a shortest path from the current position. """
return self.adjacency.a_star(self.current_pos, pos)
def get_move(self):
# check, if food is still present, otherwise go somewhere else
if (self.next_food is None or self.next_food not in self.enemy_food):
if not self.enemy_food:
# all food has been eaten? ok. i’ll stop
self.say("I am hungry.")
return stop
# all the food is in self.enemy_food
# we just pick one to go to
# (of course, there may be a smarter choice than just going random)
self.next_food = self.rnd.choice(self.enemy_food)
try:
# figure out the path to take
shortest_path = self.path_to(self.next_food)
# our next position is the last element in the path
next_pos = shortest_path[-1]
# we are a little exited about eating
# (this does not account for any food we additionally eat on our way
# to the food we have picked.)
if len(shortest_path) == 1:
self.say("Yay. Food next.")
else:
self.say("Eating in {0} steps.".format(len(shortest_path)))
# should we check for the enemy at this position?
# self.enemy_bots ?
# Naah – we risk it :)
# the difference between here and there
# is the direction we need to go to
move = diff_pos(self.current_pos, next_pos)
return move
except NoPathException:
# whoops, there is no path possible
# we better wait
return stop
def set_initial(self):
self.adjacency = AdjacencyList(self.current_uni.reachable([self.initial_pos]))
self.next_food = None
def len_of_shortest_path(layout):
uni = CTFUniverse.create(layout, 2)
al = AdjacencyList(uni.free_positions())
path = al.a_star(uni.bots[0].current_pos, uni.bots[1].current_pos)
return len(path)