NathanAlex7
c4ac5aa82e
新增兴邦金租评审项目模块, 作为 mini-OpenClaw 之上的完整交付: - 五智能体流水线(data_curator/risk_scout/case_finder/approval_advisor/risk_monitor) - evolution 自进化子系统(轨迹采集+奖励计算+模式归纳prompt增强) - /api/xingbang/* 端点(config/workflow/stream/evolution/trajectories) - 运行时产物(客户产出情报卡 + 进化轨迹数据)已 gitignore, 不含隐私 遵循兴邦三档分级审批制度, 数据源指向兴邦实验数据包
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
"""Reward Calculator — 计算一条审批轨迹的奖励分数。
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诚实分级设计 (对齐兴邦方案):
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Level 1 (现在能做,不需要用户交互):
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- 任务完成 +10
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- 工具调用成功率
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- 重复调用惩罚
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- 时间惩罚
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Level 2 (需前端加 👍/👎 按钮):
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- 用户显式反馈
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Level 3 (需人工标注):
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- 专家打分
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本文件实现 Level 1 + 预留 Level 2/3 参数。
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不做不诚实的信号: 拒绝用"还款表现"当奖励(周期1-3年无法闭环)。
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"""
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from __future__ import annotations
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from typing import Any
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def compute_reward_v1(
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trajectory: dict[str, Any],
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feedback: str | None = None,
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expert_score: float | None = None,
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) -> float:
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"""计算一条轨迹的奖励(Leve1 l 内置指标 + 可选 Level2 反馈 + Level3 专家分)。
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Args:
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trajectory: 轨迹 dict(含 steps 和 final_result)
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feedback: Level2, "positive"/"negative"
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expert_score: Level3, 1-5 分
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Returns:
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float 奖励分
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"""
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steps = trajectory.get("steps", [])
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final = trajectory.get("final_result", {})
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if not steps:
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return 0.0
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reward = 0.0
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# 1. 任务完成奖励
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if final.get("success"):
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reward += 10.0
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else:
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reward -= 10.0
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# 2. 工具调用成功率(成功工具 / 总工具)
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total_tool_calls = 0
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tool_success = 0
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for s in steps:
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tools = s.get("tools", [])
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total_tool_calls += len(tools)
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if tools:
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tool_success += 1 # 该步骤至少调了工具视为尽力(简化)
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if total_tool_calls > 0:
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reward += (tool_success / max(1, total_tool_calls)) * 5.0
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# 3. 步骤数惩罚(越少越好,常规 5 步流水线 5-15 步为佳)
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n_steps = len(steps)
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optimal = 5
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step_penalty = abs(n_steps - optimal) * 0.3
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reward -= step_penalty
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# 4. 时间惩罚(超过 600 秒 = 10 分钟开始扣)
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duration_ms = final.get("duration_ms", 0)
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if duration_ms > 600_000:
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reward -= ((duration_ms - 600_000) / 60_000) * 1.0
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# ── Level 2: 用户反馈 (可选) ──
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if feedback == "positive":
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reward += 20.0
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elif feedback == "negative":
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reward -= 10.0
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# ── Level 3: 专家打分 (可选, 1-5 归一化到 -10~+10) ──
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if expert_score is not None:
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reward += (expert_score - 3) * 5.0
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reward = max(-30.0, min(reward, 50.0)) # 裁剪
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return round(reward, 2)
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