def move(self):
self.prev_steps.append(self.position)
if algorithms.see_each_other(self.position, f.position,
position_matrix):
self.fox_last_seen.append(f.position)
else:
if len(self.fox_last_seen) > 0:
self.fox_last_seen.append(self.fox_last_seen[-1])
else:
self.fox_last_seen.append(None)
if self.fox_last_seen[-1] != None:
options = []
for (dx, dy) in [(0, 1), (1, 0), (-1, 0), (0, -1)]:
curr_position = (self.position[0] + dx, self.position[1] + dy)
if outOffBoundaries(curr_position) or isCollision(
curr_position):
continue
if len(self.prev_steps
) > 1 and curr_position == self.prev_steps[-2]:
continue
if algorithms.distance(f.position, curr_position) == 1:
continue
if number_of_bad_neighbours(curr_position) == 3:
continue
dist_from_fox = abs(curr_position[0] - self.fox_last_seen[-1]
[0]) + abs(curr_position[1] -
self.fox_last_seen[-1][1])
curr_cost = algorithms.f(12 - dist_from_fox)
curr_cost += 5 * algorithms.f(
abs(curr_position[0] - 12)) + algorithms.f(
abs(curr_position[1] - 12))
curr_cost -= 10 * self.number_of_good_bushes()
if self.fox_last_seen[-1] != None:
if not algorithms.see_each_other(curr_position,
self.fox_last_seen[-1],
position_matrix):
curr_cost -= 100
heapq.heappush(options, (curr_cost, curr_position))
if options == []:
self.position = self.prev_steps[-2]
else:
self.position = heapq.heappop(options)[1]
else:
options = []
for (dx, dy) in [(0, 1), (1, 0), (-1, 0), (0, -1)]:
curr_position = (self.position[0] + dx, self.position[1] + dy)
if outOffBoundaries(curr_position) or isCollision(
curr_position):
continue
if number_of_bad_neighbours(curr_position) == 3:
continue
if curr_position == self.position:
continue
options.append(curr_position)
self.position = random.choice(options)
self.turn += 1
def move(self):
'''
if (r.position[1], self.position[1]) in {(0, 1), (24, 23)} and abs(self.position[0] - r.position[0]) == 1:
self.position = r.position
if (r.position[0], self.position[0]) in {(0, 1), (24, 23)} and abs(self.position[1] - r.position[1]) == 1:
self.position = r.position
'''
if algorithms.see_each_other(self.position, r.position,
position_matrix):
path = algorithms.minimal_distance(self.position, r.position,
position_matrix)
self.position = path[-1]
self.rabbit_last_seen = r.position
elif self.rabbit_last_seen != None and self.rabbit_last_seen != self.position:
path = algorithms.minimal_distance(self.position, r.position,
position_matrix)
self.position = path[-1]
else:
options = []
for (dx, dy) in [(0, 1), (1, 0), (-1, 0), (0, -1)]:
curr_position = (self.position[0] + dx, self.position[1] + dy)
if outOffBoundaries(curr_position) or isCollision(
curr_position):
continue
options.append(curr_position)
self.position = random.choice(options)
def move(self):
self.prev_steps.append(self.position)
p = random.randrange(0, 1000)
if number_of_steps > 10 and self.position == self.prev_steps[-9]:
p = 0
if p > 10 and algorithms.see_each_other(self.position, r.position,
position_matrix):
path = algorithms.minimal_distance(self.position, r.position,
position_matrix)
self.position = path[-1]
self.rabbit_last_seen = r.position
elif p > 10 and self.rabbit_last_seen != None and self.rabbit_last_seen != self.position:
path = algorithms.minimal_distance(self.position, r.position,
position_matrix)
self.position = path[-1]
else:
options = []
for (dx, dy) in [(0, 1), (1, 0), (-1, 0), (0, -1)]:
curr_position = (self.position[0] + dx, self.position[1] + dy)
if outOffBoundaries(curr_position) or isCollision(
curr_position):
continue
if len(self.prev_steps
) > 1 and curr_position == self.prev_steps[-2]:
continue
options.append(curr_position)
self.position = random.choice(options)
def move(self):
if algorithms.see_each_other(self.position, f.position,
position_matrix):
self.fox_last_seen = f.position
if self.fox_last_seen != None:
options = []
for (dx, dy) in [(0, 1), (1, 0), (-1, 0), (0, -1)]:
curr_position = (self.position[0] + dx, self.position[1] + dy)
if outOffBoundaries(curr_position) or isCollision(
curr_position):
continue
if curr_position == self.prev_position:
continue
if algorithms.distance(
f.position,
curr_position) == 1 or curr_position == f.position:
continue
curr_cost = algorithms.f(
12 - (abs(curr_position[0] - self.fox_last_seen[0]) +
abs(curr_position[1] - self.fox_last_seen[1])))
curr_cost += algorithms.f(curr_position[0] -
12) + algorithms.f(curr_position[1] -
12)
if curr_position[0] in [0, 1, 24, 23] or curr_position[0] in [
0, 1, 24, 23
]:
curr_cost += 30
heapq.heappush(options, (curr_cost, curr_position))
tmp = self.prev_position
self.prev_position = self.position
if options == []:
self.position = tmp
else:
self.position = heapq.heappop(options)[1]
else:
options = []
for (dx, dy) in [(0, 1), (1, 0), (-1, 0), (0, -1)]:
curr_position = (self.position[0] + dx, self.position[1] + dy)
if outOffBoundaries(curr_position) or isCollision(
curr_position):
continue
options.append(curr_position)
self.position = random.choice(options)
self.turn += 1