152 lines
5.5 KiB
Python
152 lines
5.5 KiB
Python
# -*- coding: utf-8 -*-
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import heapq
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from collections import defaultdict
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def boss_fight(B, PlayerSkills):
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"""
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使用改进的A*算法求解Boss战斗的最优策略
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参数:
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B: Boss血量序列 [boss1_hp, boss2_hp, ...]
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PlayerSkills: 玩家技能列表 [(damage1, cooldown1), (damage2, cooldown2), ...]
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返回:
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每个Boss对应的攻击序列列表，例如：[[0, 1, 0], [2, -1, 1], ...]
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其中数字表示技能索引，-1表示等待
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如果无解则返回None
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"""
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n = len(B) # Boss数量
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m = len(PlayerSkills) # 技能数量
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if n == 0:
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return []
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# 如果所有技能伤害为0，则无法击败Boss
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if all(skill[0] == 0 for skill in PlayerSkills):
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return None
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def more_accurate_heuristic(boss_idx, boss_remaining_hp):
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if boss_idx >= n:
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return 0
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# 计算剩余总血量
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remaining_hp = boss_remaining_hp + sum(B[boss_idx + 1:])
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# 考虑技能的平均有效伤害（考虑冷却时间）
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total_damage_per_turn = 0
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for damage, cooldown in PlayerSkills:
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if damage > 0:
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# 有效伤害 = 伤害 / (冷却时间 + 1)
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effective_damage = damage / (cooldown + 1)
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total_damage_per_turn += effective_damage
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if total_damage_per_turn > 0:
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return max(1, int((remaining_hp + total_damage_per_turn - 1) / total_damage_per_turn))
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return remaining_hp
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# 使用更紧密的优先队列来保证最优性
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# 状态: (boss_idx, boss_hp, cooldown_tuple)
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start_state = (0, B[0], tuple([0] * m))
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# 优先队列: (f_score, g_score, state, path)
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queue = [(more_accurate_heuristic(0, B[0]), 0, start_state, [])]
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# 记录到达每个状态的最短路径长度
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g_score = {start_state: 0}
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while queue:
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f, g, (boss_idx, boss_hp, cooldown), path = heapq.heappop(queue)
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# 如果所有Boss都被击败
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if boss_idx >= n:
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# 重构路径为每个Boss的序列
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result_paths = [[] for _ in range(n)]
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current_boss = 0
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current_hp = B[0]
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for action in path:
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result_paths[current_boss].append(action)
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if action != -1:
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damage = PlayerSkills[action][0]
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current_hp -= damage
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if current_hp <= 0:
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current_boss += 1
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if current_boss < n:
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current_hp = B[current_boss]
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return result_paths
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current_state = (boss_idx, boss_hp, cooldown)
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# 如果已经找到到达此状态的更短路径，跳过
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if current_state in g_score and g_score[current_state] < g:
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continue
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# 生成所有可能的后继状态
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neighbors = []
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# 选项1: 等待
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new_cooldown = tuple(max(0, cd - 1) for cd in cooldown)
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neighbors.append((-1, boss_idx, boss_hp, new_cooldown))
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# 选项2: 使用每个可用技能
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for skill_idx in range(m):
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if cooldown[skill_idx] == 0: # 技能可用
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damage, skill_cooldown = PlayerSkills[skill_idx]
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new_hp = boss_hp - damage
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new_boss_idx = boss_idx
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# 更新冷却时间
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new_cooldown = list(max(0, cd - 1) for cd in cooldown)
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new_cooldown[skill_idx] = skill_cooldown
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new_cooldown = tuple(new_cooldown)
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# 检查Boss是否被击败
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if new_hp <= 0:
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new_boss_idx += 1
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if new_boss_idx < n:
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new_hp = B[new_boss_idx]
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neighbors.append((skill_idx, new_boss_idx, new_hp, new_cooldown))
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# 处理每个邻居状态
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for action, new_boss_idx, new_boss_hp, new_cooldown in neighbors:
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new_state = (new_boss_idx, new_boss_hp, new_cooldown)
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tentative_g = g + 1
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# 如果找到更好的路径到达邻居状态
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if new_state not in g_score or tentative_g < g_score[new_state]:
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g_score[new_state] = tentative_g
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h = more_accurate_heuristic(new_boss_idx, new_boss_hp)
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f_score = tentative_g + h
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new_path = path + [action]
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heapq.heappush(queue, (f_score, tentative_g, new_state, new_path))
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return None # 无解
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if __name__ == '__main__':
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# 测试用例
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B = [19, 17, 14, 19]
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PlayerSkills = [(6, 2), (9, 5), (5, 3), (4, 3), (2, 0)]
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print("Boss血量序列:", B)
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print("玩家技能 (伤害, 冷却):", PlayerSkills)
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print()
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result = boss_fight(B, PlayerSkills)
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if result:
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print("每个Boss的攻击序列:")
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for i, boss_sequence in enumerate(result):
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print(f"Boss {i+1}: {boss_sequence}")
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# 合并所有序列
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full_sequence = []
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for boss_sequence in result:
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full_sequence.extend(boss_sequence)
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print(f"完整序列: {''.join(map(str, full_sequence))}")
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print(f"总步数: {len(full_sequence)}")
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else:
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print("无解 - 无法击败所有Boss")
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