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update ai

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bridge
2025-09-07 20:24:02 +08:00
parent d83379e3a5
commit 9b85380e2e
2 changed files with 50 additions and 70 deletions
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116
src/classes/ai.py
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@@ -20,75 +20,37 @@ if TYPE_CHECKING:
class AI(ABC):
"""
AI的基类
抽象AI：统一采用批量接口。
原先的 GroupAI（多个角色的AI）语义被保留并上移到此基类。
子类需实现 _decide(world, avatars) 返回每个 Avatar 的 (action_name, action_params, thinking)。
"""
pass
class SingleAI(AI):
"""
单个角色的AI
"""
def __init__(self, avatar: Avatar):
self.avatar = avatar
@abstractmethod
async def _decide(self, world: World) -> tuple[ACTION_NAME, ACTION_PARAMS, str]:
"""
决策逻辑：决定执行什么动作和参数
由子类实现具体的决策逻辑
"""
pass
async def decide(self, world: World) -> tuple[ACTION_NAME, ACTION_PARAMS, str, Event]:
"""
决定做什么，同时生成对应的事件
"""
# 先决定动作和参数
action_name, action_params, avatar_thinking = await self._decide(world)
# 获取动作对象并生成事件
action = self.avatar.create_action(action_name)
event = action.get_event(**action_params)
return action_name, action_params, avatar_thinking, event
class GroupAI(AI):
"""
多个角色的AI
"""
def __init__(self):
self.avatars = []
async def _decide(self, world: World, avatars_to_decide: list[Avatar]) -> dict[Avatar, tuple[ACTION_NAME, ACTION_PARAMS, str]]:
"""
决策逻辑：决定执行什么动作和参数
由子类实现具体的决策逻辑
"""
pass
async def decide(self, world: World, avatars_to_decide: list[Avatar]) -> dict[Avatar, tuple[ACTION_NAME, ACTION_PARAMS, str, Event]]:
"""
决定做什么，同时生成对应的事件
决定做什么，同时生成对应的事件。
"""
# 先决定动作和参数
results = await self._decide(world, avatars_to_decide)
for avatar, result in results.items():
for avatar, result in list(results.items()):
action_name, action_params, avatar_thinking = result
action = avatar.create_action(action_name)
event = action.get_event(**action_params)
results[avatar] = (action_name, action_params, avatar_thinking, event)
# 获取动作对象并生成事件
return results
class RuleAI(SingleAI):
class RuleAI(AI):
"""
规则AI
规则AI（批量接口，内部逐个决策）
"""
async def _decide(self, world: World) -> tuple[ACTION_NAME, ACTION_PARAMS, str]:
def __decide(self, world: World, avatar: "Avatar", regions: list[Region]) -> tuple[ACTION_NAME, ACTION_PARAMS, str]:
"""
决策逻辑：决定执行什么动作和参数
单个 Avatar 的决策逻辑。
先做一个简单的：
1. 找到自己灵根对应的最好的区域
2. 检测自己是否在最好的区域
@@ -97,26 +59,42 @@ class RuleAI(SingleAI):
5. 如果需要突破境界了，则突破境界
"""
if random.random() < 0.1:
return "Play", {}, ""
best_region = self.get_best_region(list(world.map.regions.values()))
if self.avatar.is_in_region(best_region):
if self.avatar.cultivation_progress.can_break_through():
return "Breakthrough", {}, ""
return ("Play", {}, "")
best_region = self.get_best_region_for_avatar(avatar, regions)
if avatar.is_in_region(best_region):
if avatar.cultivation_progress.can_break_through():
return ("Breakthrough", {}, "")
else:
return "Cultivate", {}, ""
return ("Cultivate", {}, "")
else:
return "MoveToRegion", {"region": best_region.name}, ""
def get_best_region(self, regions: list[Region]) -> Region:
return ("MoveToRegion", {"region": best_region.name}, "")
async def _decide(self, world: World, avatars_to_decide: list[Avatar]) -> dict[Avatar, tuple[ACTION_NAME, ACTION_PARAMS, str]]:
"""
决策逻辑：批量接口的实现上，逐个 Avatar 调用 __decide 进行独立决策，
以保持规则AI的可控性与可测试性。
"""
results: dict[Avatar, tuple[ACTION_NAME, ACTION_PARAMS, str]] = {}
regions: list[Region] = list(world.map.regions.values())
for avatar in avatars_to_decide:
results[avatar] = self.__decide(world, avatar, regions)
return results
def get_best_region_for_avatar(self, avatar: "Avatar", regions: list[Region]) -> Region:
"""
根据avatar的灵根找到最适合的区域
"""
root = self.avatar.root
essence_type = corres_essence_type[root]
region_with_best_essence = max(regions, key=lambda region: region.essence.get_density(essence_type))
essence_type = corres_essence_type[avatar.root]
region_with_best_essence = max(
regions, key=lambda region: region.essence.get_density(essence_type)
)
return region_with_best_essence
class LLMAI(GroupAI):
class LLMAI(AI):
"""
LLM AI
一些思考：
@@ -126,6 +104,7 @@ class LLMAI(GroupAI):
不能每个单步step都调用一次LLM来决定下一步做什么。这样子一方面动作一直乱变，另一方面也太费token了。
decide的作用是，拉取既有的动作链（如果没有了就call_llm），再根据动作链决定动作，以及动作之间的衔接。
"""
async def _decide(self, world: World, avatars_to_decide: list[Avatar]) -> dict[Avatar, tuple[ACTION_NAME, ACTION_PARAMS, str]]:
"""
异步决策逻辑：通过LLM决定执行什么动作和参数
@@ -134,15 +113,16 @@ class LLMAI(GroupAI):
avatar_infos = {avatar.id: avatar.get_prompt() for avatar in avatars_to_decide}
info = {
"avatar_infos": avatar_infos,
"global_info": global_info
"global_info": global_info,
}
res = await get_ai_prompt_and_call_llm_async(info)
results = {}
results: dict[Avatar, tuple[ACTION_NAME, ACTION_PARAMS, str]] = {}
for avatar in avatars_to_decide:
action_name, action_params, avatar_thinking = res[avatar.id]["action_name"], res[avatar.id]["action_params"], res[avatar.id]["avatar_thinking"]
action_name = res[avatar.id]["action_name"]
action_params = res[avatar.id]["action_params"]
avatar_thinking = res[avatar.id]["avatar_thinking"]
results[avatar] = (action_name, action_params, avatar_thinking)
return results
llm_ai = LLMAI()
# rule_ai = RuleAI()
rule_ai = None
rule_ai = RuleAI()

4
static/config.yml
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@@ -7,8 +7,8 @@ paths:
templates: static/templates/
ai:
mode: "llm" # "rule" or "llm"
mode: "rule" # "rule" or "llm"
game:
init_npc_num: 3
npc_birth_rate_per_month: 0.001
npc_birth_rate_per_month: 0.01