diff options
| -rw-r--r-- | strategy.py | 95 |
1 files changed, 51 insertions, 44 deletions
diff --git a/strategy.py b/strategy.py index 3d5721a..018187e 100644 --- a/strategy.py +++ b/strategy.py @@ -11,7 +11,7 @@ friendly_players=["Windfisch","windfisch","Cyanide","cyanide"] +\ class Strategy: def __init__(self, c, gui=None): self.target = (0,0) - self.has_target = False + self.target_type = None self.target_cell = None self.color = (0,0,0) self.c = c @@ -48,12 +48,15 @@ class Strategy: return False def splitkiller(self, cell): - return not cell.is_virus and not cell.is_food and cell.mass > 1.25*2*self.get_my_smallest().mass + return not cell.is_virus and not cell.is_food and cell.mass+20 > 1.25*2*self.get_my_smallest().mass and cell.mass / 15. < self.c.player.total_mass + + def hugecell(self, cell): + return not cell.is_virus and not cell.is_food and cell.mass / 15. >= self.c.player.total_mass def nonsplitkiller(self, cell): - return not cell.is_virus and not cell.is_food and 1.20*self.get_my_smallest().mass < cell.mass and cell.mass < 1.25*2*self.get_my_smallest().mass + return not cell.is_virus and not cell.is_food and 1.20*self.get_my_smallest().mass < cell.mass and cell.mass+20 < 1.25*2*self.get_my_smallest().mass - def quality(self, cell, myspeed): + def quality(self, cell, cells, myspeed): dd_sq = max((cell.pos[0]-self.c.player.center[0])**2 + (cell.pos[1]-self.c.player.center[1])**2,0.001) sigma = 500 * max(cell.mass,1) # TODO FIXME don't try to eat running away cells if mechanics.speed(cell) - myspeed >= 0: @@ -61,9 +64,11 @@ class Strategy: dist_score = -math.exp(-dd_sq/(2*sigma**2)) - rivals = filter(lambda r : self.rival(r,cell), self.c.world.cells.values()) - splitkillers = filter(self.splitkiller, self.c.world.cells.values()) - nonsplitkillers = filter(self.nonsplitkiller, self.c.world.cells.values()) + + rivals = filter(lambda r : self.rival(r,cell), cells) + hugecells = filter(self.hugecell, cells) + splitkillers = filter(self.splitkiller, cells) + nonsplitkillers = filter(self.nonsplitkiller, cells) rival_score = 0 for r in rivals: @@ -71,49 +76,39 @@ class Strategy: sigma = r.size + 100 rival_score += math.exp(-dd_sq/(2*sigma**2)) + hugecell_score = 0 + for s in hugecells: + dd_sq = max(0.001, (s.pos[0]-cell.pos[0])**2 + (s.pos[1]-cell.pos[1])**2) + sigma = s.size + 10 + hugecell_score += math.exp(-dd_sq/(2*sigma**2)) + splitkill_score = 0 for s in splitkillers: dd_sq = max(0.001, (s.pos[0]-cell.pos[0])**2 + (s.pos[1]-cell.pos[1])**2) - sigma = (500+2*s.size) + sigma = s.size + 650 + 250 splitkill_score += math.exp(-dd_sq/(2*sigma**2)) nonsplitkill_score = 0 for s in nonsplitkillers: dd_sq = max(0.001, (s.pos[0]-cell.pos[0])**2 + (s.pos[1]-cell.pos[1])**2) - sigma = (300+s.size) + sigma = (75+s.size) + 250 nonsplitkill_score += math.exp(-dd_sq/(2*sigma**2)) density_score = 0 sigma = 300 - for f in filter(lambda c : c.is_food and c!=cell, self.c.world.cells.values()): - dd_sq = (f.pos[0]-cell.pos[0])**2 + (f.pos[1]-cell.pos[1])**2 - density_score -= math.exp(-dd_sq/(2*sigma**2)) + #for f in filter(lambda c : c.is_food and c!=cell, self.c.world.cells.values()): + # dd_sq = (f.pos[0]-cell.pos[0])**2 + (f.pos[1]-cell.pos[1])**2 + # density_score -= math.exp(-dd_sq/(2*sigma**2)) wall_score = 0 wall_dist = min( cell.pos[0]-self.c.world.top_left[1], self.c.world.bottom_right[1]-cell.pos[0], cell.pos[1]-self.c.world.top_left[0], self.c.world.bottom_right[0]-cell.pos[1] ) sigma = 100 wall_score = math.exp(-wall_dist**2/(2*sigma**2)) - return 2.5*dist_score + 0.2*rival_score + nonsplitkill_score + 5*splitkill_score + 0.1*density_score + 5*wall_score + return 0.5*dist_score + 0.2*rival_score + 5.0*hugecell_score + 5.0*nonsplitkill_score + 15*splitkill_score + 0.1*density_score + 5*wall_score ##print (density_score) #return density_score - def weight_cell(self, cell): - df = (10/self.dist(cell)) - if self.edible(cell): - quality = self.quality(cell) - if cell.is_food: - return 1 + cell.mass * df * quality - else: - mf = 1 / ((self.get_my_smallest().mass * 0.75) + 1) - cell.mass - return cell.mass * df * quality * mf - elif self.threat(cell): - if cell.is_virus: - return -cell.mass * df * 100 - else: - return -cell.mass * df - else: - return 0 def process_frame(self): runaway = False @@ -121,6 +116,7 @@ class Strategy: my_smallest = min(self.c.player.own_cells, key=lambda cell : cell.mass) my_largest = max(self.c.player.own_cells, key=lambda cell : cell.mass) + cells = filter(lambda r : not r.is_food and not r.is_virus, self.c.world.cells.values()) friendly_cells = list(filter(lambda c : c.is_virus or c.name in friendly_players, self.c.world.cells.values())) if friendly_cells: @@ -144,7 +140,7 @@ class Strategy: self.gui.hilight_cell(friend_to_feed, (255,255,255),(255,127,127),30) self.target_cell = friend_to_feed - self.has_target = True + self.target_type = 'friend' if self.do_approach_friends: for c in self.c.player.own_cells: @@ -226,7 +222,7 @@ class Strategy: else: allowed_dist = "don't care" - if allowed_dist != "don't care" and dist < allowed_dist: + if allowed_dist != "don't care" and dist < allowed_dist and False: try: angle = math.atan2(relpos[1],relpos[0]) corridor_halfwidth = math.asin(min(1, cell.size / dist)) @@ -264,7 +260,7 @@ class Strategy: runaway_x, runaway_y = (self.c.player.center[0]+int(100*math.cos(runaway_angle))), (self.c.player.center[1]+int(100*math.sin(runaway_angle))) self.target = (runaway_x, runaway_y) - self.has_target = False + self.target_type = None self.target_cell = None self.color = (255,0,0) @@ -273,32 +269,43 @@ class Strategy: for i in forbidden_intervals: self.gui.draw_arc(self.c.player.center, self.c.player.total_size+10, i, (255,0,255)) - # if however there's no enemy to avoid, try to feed a friend. or chase food or jizz randomly around + # if however there's no enemy to avoid, try to feed a friend. or chase food or fly randomly around else: if self.target_cell != None: self.target = tuple(self.target_cell.pos) + + # check if target went out of sight, or became infeasible if self.target_cell not in self.c.world.cells.values() or (not self.edible(self.target_cell) and not self.target_cell in friendly_cells): self.target_cell = None - self.has_target = False + self.target_type = None + elif self.target == tuple(self.c.player.center): - self.has_target = False + self.target_type = None print("Reached random destination") - if not self.has_target: + if not self.target_type == 'friend': # i.e. None, random or food food = list(filter(self.edible, self.c.world.cells.values())) - food = sorted(food, key = lambda c : self.quality(c, mechanics.speed(my_largest))) + myspeed = mechanics.speed(my_largest) + food = sorted(food, key = lambda c : self.quality(c, cells, myspeed)) if len(food) > 0: - self.target = (food[0].pos[0], food[0].pos[1]) - self.target_cell = food[0] - - self.has_target = True - self.color = (0,0,255) - else: + food_candidate = food[0] + + if self.target_type == None or self.target_type == 'random' or (self.target_type == 'food' and self.quality(food_candidate, cells, myspeed) < self.quality(self.target_cell, cells, myspeed)-1): + if self.target_type == 'food': + print("abandoning food of value %.3f for %.3f" % (self.quality(self.target_cell, cells, myspeed),self.quality(food_candidate, cells, myspeed))) + + self.target_cell = food_candidate + self.target = (self.target_cell.pos[0], self.target_cell.pos[1]) + + self.target_type = 'food' + self.color = (0,0,255) + + if self.target == None: rx = self.c.player.center[0] + random.randrange(-400, 401) ry = self.c.player.center[1] + random.randrange(-400, 401) self.target = (rx, ry) - self.has_target = True + self.target_type = 'random' self.color = (0,255,0) print("Nothing to do, heading to random targetination: " + str((rx, ry))) |