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cs.LG2025
Approximating Shapley Explanations in Reinforcement Learning
Daniel Beechey, Ãzgür ÅimÅek
Reinforcement learning has achieved remarkable success in complex decision-making environments, yet its lack of transparency limits its deployment in practice, especially in safety…
cs.LG2025
A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values
Daniel Beechey, Thomas M. S. Smith, Ãzgür ÅimÅek
Reinforcement learning agents can achieve super-human performance in complex decision-making tasks, but their behaviour is often difficult to understand and explain. This lack of e…