update ai

src/classes/action.py

@@ -150,8 +150,13 @@ class Move(DefineAction, ChunkActionMixin):
         移动到某个tile
         """
         world = self.world
-        new_x = self.avatar.pos_x + delta_x
-        new_y = self.avatar.pos_y + delta_y
+        # 基于境界的移动步长：每轴最多移动 move_step_length 格
+        step = getattr(self.avatar, "move_step_length", 1)
+        clamped_dx = max(-step, min(step, delta_x))
+        clamped_dy = max(-step, min(step, delta_y))
+
+        new_x = self.avatar.pos_x + clamped_dx
+        new_y = self.avatar.pos_y + clamped_dy
 
         # 边界检查：越界则不移动
         if world.map.is_in_bounds(new_x, new_y):
@@ -180,9 +185,10 @@ class MoveToRegion(DefineAction, ActualActionMixin):
         region_center_loc = region.center_loc
         delta_x = region_center_loc[0] - cur_loc[0]
         delta_y = region_center_loc[1] - cur_loc[1]
-        # 横纵向一次最多移动一格（可以同时横纵移动）
-        delta_x = max(-1, min(1, delta_x))
-        delta_y = max(-1, min(1, delta_y))
+        # 横纵向一次最多移动 move_step_length 格（可以同时横纵移动）
+        step = getattr(self.avatar, "move_step_length", 1)
+        delta_x = max(-step, min(step, delta_x))
+        delta_y = max(-step, min(step, delta_y))
         Move(self.avatar, self.world).execute(delta_x, delta_y)
 
     def is_finished(self, region: Region|str) -> bool:

src/classes/ai.py

@@ -114,7 +114,7 @@ class LLMAI(AI):
         异步决策逻辑：通过LLM决定执行什么动作和参数
         """
         global_info = world.get_info()
-        avatar_infos = {avatar.id: avatar.get_prompt() for avatar in avatars_to_decide}
+        avatar_infos = {avatar.name: avatar.get_prompt() for avatar in avatars_to_decide}
         info = {
             "avatar_infos": avatar_infos,
             "global_info": global_info,
@@ -122,9 +122,9 @@ class LLMAI(AI):
         res = await get_ai_prompt_and_call_llm_async(info)
         results: dict[Avatar, tuple[ACTION_NAME, ACTION_PARAMS, str]] = {}
         for avatar in avatars_to_decide:
-            action_name = res[avatar.id]["action_name"]
-            action_params = res[avatar.id]["action_params"]
-            avatar_thinking = res[avatar.id]["avatar_thinking"]
+            action_name = res[avatar.name]["action_name"]
+            action_params = res[avatar.name]["action_params"]
+            avatar_thinking = res[avatar.name]["avatar_thinking"]
             results[avatar] = (action_name, action_params, avatar_thinking)
         return results
 

src/classes/avatar.py

@@ -297,6 +297,13 @@ class Avatar:
         
         return f"{info}\n其个性为：{persona}\n{magic_stone_info}\n{items_info}\n决策时需参考这个角色的个性。\n该角色的动作空间及其参数为：{action_space}"
 
+    @property
+    def move_step_length(self) -> int:
+        """
+        获取角色的移动步长
+        """
+        return int(self.cultivation_progress.realm.value)
+
 def get_new_avatar_from_ordinary(world: World, current_month_stamp: MonthStamp, name: str, age: Age):
     """
     从凡人中来的新修士

static/templates/ai.txt

@@ -1,12 +1,12 @@
 你是一个决策者，这是一个修仙的仙侠世界，你负责来决定一些NPC的下一步行为。
 {global_info}
-你需要进行决策的NPC的dict[AvatarId, info]为
+你需要进行决策的NPC的dict[AvatarName, info]为
 {avatar_infos}
 
 注意，只返回json格式的结果。
 分Avatar进行返回，格式为：
 {{
-    AvatarId: {{
+    AvatarName: {{
         "thinking": ..., // 简单思考应该怎么决策
         "action_name": ...,
         "action_params": ...,