Auditing the Risk Claims of Distributional Reinforcement Learning

Distributional reinforcement learning agents learn full return distributions that are increasingly read at face value: for interpretability, risk-sensitive control, and safety monitoring. We ask a question theory anticipates but that has not been measured directly: are the risk claims of a trained distributional agent true? Our audit combines a decision-relevant screening metric (the excess Wasserstein gap between the top two actions, which equals the mass by which first-order stochastic dominan

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ethics.ai (13 July 2026), “Auditing the Risk Claims of Distributional Reinforcement Learning,” evidence record 3038, https://ethics.ai/record/3038 (originally published by arXiv cs.AI).

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