Researchers propose SPOT, a model-agnostic framework for interpreting deep reinforcement learning policies via lookahead trees

Researchers have introduced SPOT (Sampling Policy Observation Tree), a model-agnostic, sampling-based framework designed to interpret deep reinforcement learning (DRL) policies by constructing interpretable finite-horizon trees. The method samples actions and recursively simulates successor states to empirically represent a policy’s action preferences and their downstream evolution.

The framework provides formal guarantees, including asymptotic recovery of the policy’s unique most probable action and characterization of disagreement behavior under high-entropy policies. These guarantees aim to ground the interpretability claims in measurable properties of the policy’s decision-making process.

In a case study, the authors demonstrate SPOT in the SUMO-RL traffic-signal control domain. The tree-based representation enables inspection of policy preferences, comparison of alternative future trajectories, and revelation of downstream behaviors that are not apparent through single-timestep feature-attribution methods.

The approach is positioned as a practical tool for auditing DRL policies, particularly in complex environments where decisions unfold over multiple timesteps. By making policy preferences and potential outcomes explicit, SPOT could support debugging, compliance, and safety evaluations in domains such as autonomous driving, robotics, and infrastructure management.