Approximating Shapley Explanations in Reinforcement Learning
Daniel Beechey, Özgür Şimşek
Reinforcement learning has achieved remarkable success in complex decision-making environments, yet its lack of transparency limits deployment in critical settings. Shapley values provide a principled framework for explaining reinforcement learning, but their exponential computational cost makes them impractical for real-world problems. We address this challenge by introducing FastSVERL, a scalable method for approximating Shapley values 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. These contributions position FastSVERL as a practical solution for real-time, Shapley-based interpretability in reinforcement learning.