5ae63d9df9
- 扩展 AgentState 添加 action_points, max_action_points, last_action_tick 字段 - 新增 ActionFeedback 模型用于返回行动执行结果 - 创建 action_points.py 模块实现行动点消耗与恢复逻辑 - 行动消耗表: vote=1, trigger_skill=2, influence=2, comment/support/chaos=0 - 每 tick 恢复 1 点行动点（不超过 max） - 行动点不足时拒绝执行并返回失败反馈 - 新增 7 个测试用例，全部 37 个测试通过 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
"""行动点系统 - 管理用户行动点的消耗与恢复"""
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from typing import List, Tuple, Dict
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from .models import WorldState, Event, ActionFeedback
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# 行动消耗表
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ACTION_COST: Dict[str, int] = {
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"vote": 1,
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"trigger_skill": 2,
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"influence": 2,
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"comment": 0,
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"support": 0,
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"chaos": 0,
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}
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DEFAULT_COST = 0
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def get_action_cost(event_type: str) -> int:
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"""获取行动消耗的点数"""
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return ACTION_COST.get(event_type, DEFAULT_COST)
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def check_action_points(state: WorldState, user: str, cost: int) -> bool:
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"""检查用户是否有足够的行动点"""
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if user not in state.agents:
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return True # 非 agent 用户不受限制
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return state.agents[user].action_points >= cost
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def consume_action_points(
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state: WorldState, user: str, cost: int
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) -> None:
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"""消耗行动点"""
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if user not in state.agents:
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return
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agent = state.agents[user]
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agent.action_points = max(0, agent.action_points - cost)
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agent.last_action_tick = state.tick
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def regenerate_action_points(state: WorldState) -> None:
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"""每 tick 恢复行动点"""
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for agent_id, agent in state.agents.items():
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if state.tick - agent.last_action_tick >= 1:
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if agent.action_points < agent.max_action_points:
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agent.action_points = min(
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agent.max_action_points,
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agent.action_points + 1
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)
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def process_event_with_ap(
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state: WorldState, event: Event
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) -> Tuple[bool, ActionFeedback]:
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"""处理单个事件的行动点检查
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返回: (是否允许执行, 反馈信息)
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"""
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user = event.user
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cost = get_action_cost(event.type)
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# 0 消耗的行动不需要检查
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if cost == 0:
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return True, ActionFeedback(
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success=True,
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reason="action applied",
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remaining_ap=state.agents[user].action_points if user in state.agents else 0,
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user=user
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)
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# 检查行动点
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if not check_action_points(state, user, cost):
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remaining = state.agents[user].action_points if user in state.agents else 0
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return False, ActionFeedback(
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success=False,
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reason=f"insufficient action points (need {cost}, have {remaining})",
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remaining_ap=remaining,
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user=user
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)
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# 消耗行动点
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consume_action_points(state, user, cost)
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remaining = state.agents[user].action_points if user in state.agents else 0
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return True, ActionFeedback(
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success=True,
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reason="action applied",
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remaining_ap=remaining,
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user=user
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)
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