  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.

Machine Learning;Artificial Intelligence