评估智能体时，仅看结果会误判风险，需结合行为轨迹检测隐性违规。
When Outcome Looks Right But Discipline Fails: Trace-Based Evaluation Under Hidden Competitor State
- 基于行为轨迹设计新评估范式，追踪部署环境中的真实决策过程
- 在两家酒店和竞价任务中，纯奖励优化的策略无法保持定价纪律
- 适合关注多智能体系统安全性和可解释性的研究人员使用
仅依赖结果的评估方式可能认证出经济不安全的智能体：一个策略虽达成业务关键绩效指标，却违反可部署的行为规范。在存在隐藏竞争者状态的酒店定价场景中，学习者可实现合理的每间可售房收入（RevPAR），但无法维持基于规则的收益管理竞争者的价格纪律。本文提出“纪律稳定性”这一基于轨迹的评估范式：定义基准行为，限制观测于部署环境，从失败中推导轨迹诊断，通过消融实验分离机制，并测试迁移与部署表现。在双酒店基准和紧凑型隐藏预算竞价任务中，仅优化奖励的PPO变体未能对齐轨迹；揭示隐藏状态可降低标签不确定性；确定性复制能消除不确定性；而带有轨迹先验或历史修正的策略更能保持价格或出价分布。纯粹的行为克隆在对称模仿中已足够，而轨迹先验强化学习在能力不对称下提供有界适应性。本文贡献在于建立一种评估与基准范式，而非新型优化器或关于多智能体强化学习的普适主张。
原文摘要 · Abstract (English)
Outcome-only evaluation can certify economically unsafe agents: a policy can hit a business KPI while violating deployable behavioral discipline. In hotel pricing with hidden competitor state, a learner can achieve plausible revenue per available room while failing to preserve the rate discipline of a rule-based revenue-management competitor. We introduce discipline stability, a trace-based evaluation paradigm: define the benchmark behavior, restrict observations to the deployment regime, induce trace diagnostics from failure, separate mechanisms with ablations, and test transfer and deployment. Across a two-hotel benchmark and a compact hidden-budget bidding task, reward-only PPO variants miss trace alignment; revealing hidden state reduces label uncertainty; deterministic copy collapses uncertainty; and trace-prior or corrected history policies better preserve price or bid distributions. Pure behavior cloning is nearly enough for symmetric imitation, while Trace-Prior RL adds bounded adaptation under capacity asymmetry. The contribution is an evaluation and benchmark paradigm, not a new optimizer or a universal claim about MARL
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