SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
A new sampling-based framework brings interpretability to complex deep reinforcement learning decisions.
Deep reinforcement learning (DRL) agents achieve strong performance in complex environments, yet their decision-making processes remain difficult to interpret. We introduce SPOT (Sampling Policy Observation Tree), a novel model-agnostic, sampling-based framework for interpreting ...
ArXiv CS.AI
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SPOT moves beyond opaque feature-attribution methods by visualizing the long-term decision-making trajectories of reinforcement learning agents. This transparency is crucial for deploying autonomous systems in high-stakes environments like traffic management where understanding future outcomes is as important as immediate actions.
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