
SPOT Reveals Deep RL Decisions by Simulating the Future
SPOT is a model-agnostic, sampling-based framework that explains deep reinforcement learning policies through finite-horizon action trees. By recursively simulating successor states, it exposes action preferences and downstream behaviors that single-step attribution methods may miss.
ArXiv AI · 44d ago
















