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:摘要:从观测数据中学习智能体行为,可以提高我们理解他们的决策过程,推进我们解释他们与环境和其他智能体的交互的能力。虽然文献中已提出了多种学习技术,但尚未探索具有一个特殊环境的多智能体系统,即智能体身份仍然匿名的情况。例如,金融市场中标记数据用于标识市场参与者策略通常是专有的,只有由多个市场参与者交互产生的匿名状态-动作对是公开的。因此,智能体动作序列不可观测,限制了现有工作的适用性。在本文中,我们提出了一种称为 K-SHAP 的策略聚类算法,它能够根据智能体策略将匿名状态-动作对分组。我们将该问题作为模仿学习(IL)任务,并学习一个能够模仿不同环境状态下所有智能体行为的世界策略。我们利用该世界策略通过一种名为 SHAP(Shapley Additive Explanations)的加性特征归因方法来解释每个匿名观测。最后,通过聚类解释,我们展示了能够识别不同智能体策略并相应地对观测进行分组的能力。我们在模拟的合成市场数据和真实的金融数据集上评估了我们的方法。我们证明了我们的方法明显且一致地优于现有方法,识别不同的智能体策略。