Auditing the Risk Claims of Distributional Reinforcement Learning

A new audit of distributional reinforcement learning agents (QR-DQN, C51, IQN) on MinAtar and Atari games finds that 40-95% of the strongest claimed risk trade-offs are refuted at 95% confidence, with the learned "risk" reflecting a training artifact rather than environment stochasticity. The artifact is structural, appears unchanged at full-Atari scale, and persists even with risk training or ensembling, indicating that the agents' risk claims are uninformative.

Computer Science Artificial Intelligence Submitted on 13 Jul 2026 Title:Auditing the Risk Claims of Distributional Reinforcement Learning View PDF /pdf/2607.11607 HTML experimental https://arxiv.org/html/2607.11607v1 Abstract: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 dominance is violated , ground truth from snapshot-restart Monte Carlo, and a statistical harness permutation nulls, bootstrap refutation, FDR control without which the audit itself manufactures false conclusions. Across QR-DQN, C51, and IQN on MinAtar 33 runs , 40-95% of the strongest claimed risk trade-offs are refuted at 95% confidence, the placement of the strongest claims is statistically indistinguishable from truth-blind, and essentially no claim is confirmable: for these agents, the learned "risk" reflects a training artifact rather than environment stochasticity. The artifact is structural fully formed early in training, uncorrelated with final score, idiosyncratic to each seed and appears unchanged at full-Atari scale, with every top Breakout claim of a pretrained near-state-of-the-art QR-DQN refuted. Positive controls of known magnitude confirm 96-100% of real claims correlation 0.89-0.92 : the reading measures the agents, not the audit. Acting on the heads' CVaR advice at their most-flagged states ranges from beneficial to significantly worse than chance. Neither training for risk nor ensembling removes the artifact, and recalibration passes the audit only by nullifying the claims: the head is uninformative, not merely miscalibrated. We release the toolkit and document two silent pitfalls that produced convincing but wrong audits of our own. Current browse context: cs.AI References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .