SPOT: A New Framework for Explaining Deep Reinforcement Learning Decisions in Intelligent Control

SPOT is a model-agnostic framework for interpreting deep reinforcement learning policies by building finite-horizon trees through sampling and simulation, providing an empirical view of action preferences and future outcomes.

Unlike single-step feature attribution, SPOT offers multi-step lookahead explanations, revealing downstream behaviors and enabling comparison of alternative future trajectories.

Yes, especially in intelligent traffic signal control, helping optimize traffic flow and reduce congestion, aligning with smart city visions in countries like UAE and Saudi Arabia.

It requires access to an environment simulator, and simulation costs may be high for complex systems; performance depends on sampling quality in high-entropy policies.