diff options
| -rw-r--r-- | strategy.py | 58 |
1 files changed, 56 insertions, 2 deletions
diff --git a/strategy.py b/strategy.py index 0cba570..1f1249f 100644 --- a/strategy.py +++ b/strategy.py @@ -65,6 +65,19 @@ class Strategy: # if however there's no enemy to avoid, chase food or jizz randomly around else: def edible(cell): return (cell.is_food) or (cell.mass <= sorted(c.player.own_cells, key = lambda x: x.mass)[0].mass * 0.75) and not (cell.is_virus) + def rival(cell, food): + if cell.is_virus or cell.is_food: return False + if cell.cid in c.player.own_ids: return False + + if cell.mass < 1.25*my_smallest: + return food.is_food or cell.size > 1.25*food.size + else: + return False + def splitkiller(cell): + return not cell.is_virus and not cell.is_food and cell.mass > 1.25*2*my_smallest + + def nonsplitkiller(cell): + return not cell.is_virus and not cell.is_food and 1.20*my_smallest < cell.mass and cell.mass < 1.25*2*my_smallest if self.target_cell != None: self.target = tuple(self.target_cell.pos) @@ -79,8 +92,49 @@ class Strategy: if not self.has_target: food = list(filter(edible, c.world.cells.values())) - def dist(cell): return math.sqrt((cell.pos[0]-c.player.center[0])**2 + (cell.pos[1]-c.player.center[1])**2) - food = sorted(food, key = dist) + def quality(cell): + dd_sq = max((cell.pos[0]-c.player.center[0])**2 + (cell.pos[1]-c.player.center[1])**2,0.001) + sigma = 500 + dist_score = -math.exp(-dd_sq/(2*sigma**2)) + + rivals = filter(lambda r : rival(r,cell), c.world.cells.values()) + splitkillers = filter(splitkiller, c.world.cells.values()) + nonsplitkillers = filter(nonsplitkiller, c.world.cells.values()) + + rival_score = 0 + for r in rivals: + dd_sq = max(0.001, (r.pos[0]-cell.pos[0])**2 + (r.pos[1]-cell.pos[1])**2) + sigma = r.size + 100 + rival_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) + 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) + 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, 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]-c.world.top_left[1], c.world.bottom_right[1]-cell.pos[0], cell.pos[1]-c.world.top_left[0], c.world.bottom_right[0]-cell.pos[1] ) + sigma = 100 + wall_score = math.exp(-wall_dist**2/(2*sigma**2)) + + return dist_score + 0.2*rival_score + nonsplitkill_score + 5*splitkill_score + 0.1*density_score + 5*wall_score + ##print (density_score) + #return density_score + + food = sorted(food, key = quality) if len(food) > 0: self.target = (food[0].pos[0], food[0].pos[1]) |