The Ambiguity Scale behind Dual Dynamics Protection
Abstract
Dual robustness separates training and deployment shifts, while Wasserstein Bellman computation protects a defined transition set. Evaluation should preserve those distinctions and vary their scales separately. Faster updates cannot establish that the chosen uncertainty description includes the relevant departure. Independent shifted-environment outcomes are needed before the robust action's practical value is claimed. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
References
Z. Qiao, R. Yang, J. Lyu, X. Li, Z. Dai, Z. Yang, S. Gao, and S. Qiu, “Dual-Robust Cross-Domain Offline Reinforcement Learning Against Dynamics Shifts,” Proceedings of the 2026 International Conference on Learning Representations, 2026.
Z. Yu, L. Dai, S. Xu, S. Gao, and C. Ho, “Fast Bellman Updates for Wasserstein Distributionally Robust MDPs,” Advances in Neural Information Processing Systems, pp. 30554–30578, 2023.
Sang, Yinghao. “Adaptive Quantization Strategies for Robust ML Inference Under Distribution Shift.” Proceedings of the 2026 5th International Conference on Cyber Security, Artificial Intelligence and Digital Economy, ACM, 2026, pp. 362-368. https://doi.org/10.1145/3819836.3819892.
Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.
Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
Hu, Edward J., et al. “LoRA: Low-Rank Adaptation of Large Language Models.” International Conference on Learning Representations, 2022. https://openreview.net/forum?id=nZeVKeeFYf9.
Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, PMLR, vol. 70, 2017, pp. 1321–1330. https://proceedings.mlr.press/v70/guo17a.html.
Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. 2nd ed., MIT Press, 2018. http://incompleteideas.net/book/the-book-2nd.html.
Levine, Sergey, Aviral Kumar, George Tucker, and Justin Fu. “Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems.” arXiv, 2020, arXiv:2005.01643. https://arxiv.org/abs/2005.01643.
Kumar, Aviral, Aurick Zhou, George Tucker, and Sergey Levine. “Conservative Q-Learning for Offline Reinforcement Learning.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 1179–1191. https://arxiv.org/abs/2006.04779.
Lewis, Patrick, et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–9474. https://arxiv.org/abs/2005.11401.
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