Learning agent behaviors from observational data has shown to improve our understanding of their decision-making processes, advancing our ability to explain their interactions with the environment and other agents. While multiple learning techniques have been proposed in the literature, there is one particular setting that has not been explored yet: multi agent systems where agent identities remain anonymous. For instance, in financial markets labeled data that identifies market participant strategies is typically proprietary, and only the anonymous state-action pairs that result from the interaction of multiple market participants are publicly available. As a result, sequences of agent actions are not observable, restricting the applicability of existing work. In this paper, we propose a Policy Clustering algorithm, called K-SHAP, that learns to group anonymous state-action pairs according to the agent policies. We frame the problem as an Imitation Learning (IL) task, and we learn a world-policy able to mimic all the agent behaviors upon different environmental states. We leverage the world-policy to explain each anonymous observation through an additive feature attribution method called SHAP (SHapley Additive exPlanations). Finally, by clustering the explanations we show that we are able to identify different agent policies and group observations accordingly. We evaluate our approach on simulated synthetic market data and a real-world financial dataset. We show that our proposal significantly and consistently outperforms the existing methods, identifying different agent strategies.

翻译：从观测数据中学习智能体行为，有助于增进对其决策过程的理解，提升我们解释其与环境及其他智能体交互的能力。尽管已有多种学习技术被提出，但尚未探索一个特定场景：智能体身份保持匿名的多智能体系统。例如，在金融市场中，标识市场参与者策略的标注数据通常属于专有信息，唯有多个市场参与者交互产生的匿名状态-动作对可供公开获取。因此，智能体的动作序列不可观测，限制了现有方法的适用性。本文提出一种名为K-SHAP的策略聚类算法，该算法能够学习根据智能体策略对匿名状态-动作对进行分组。我们将该问题建模为模仿学习任务，并学习一个能够模拟不同环境状态下所有智能体行为的全局策略。通过利用全局策略，我们借助一种名为SHAP（沙普利加法解释）的加性特征归因方法解释每个匿名观测。最后，通过对解释结果进行聚类，我们成功识别不同智能体策略并据此分组观测。我们在模拟合成市场数据和真实金融数据集上进行评估，结果表明所提方法显著且持续优于现有方法，能够有效识别不同智能体策略。