Approximating Shapley Explanations in Reinforcement Learning

Daniel Beechey; Özgür Şimşek

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arXiv · 2511.06094

Approximating Shapley Explanations in Reinforcement Learning

Daniel Beechey·Özgür Şimşek

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Abstract

Reinforcement learning has achieved remarkable success in complex decision-making environments, yet its lack of transparency limits its deployment in practice, especially in safety-critical settings. Shapley values from cooperative game theory provide a principled framework for explaining reinforcement learning; however, the computational cost of Shapley explanations is an obstacle to their use. We introduce FastSVERL, a scalable method for explaining reinforcement learning by approximating Shapley values. FastSVERL is designed to handle the unique challenges of reinforcement learning, including temporal dependencies across multi-step trajectories, learning from off-policy data, and adapting to evolving agent behaviours in real time. FastSVERL introduces a practical, scalable approach for principled and rigorous interpretability in reinforcement learning.

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BibTeX RIS

Daniel Beechey, Özgür Şimşek. 2025-11-08. Approximating Shapley Explanations in Reinforcement Learning. https://arxiv.org/abs/2511.06094

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