Summary of Statistical Learning Of Distributionally Robust Stochastic Control in Continuous State Spaces, by Shengbo Wang et al.
Statistical Learning of Distributionally Robust Stochastic Control in Continuous State Spaces
by Shengbo Wang, Nian Si, Jose Blanchet, Zhengyuan Zhou
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces a novel approach to controlling stochastic systems with potentially continuous state and action spaces, which is particularly useful in engineering settings where distributional shifts may occur. The existing methods assume independent and identically distributed noise processes, but the new paradigm accommodates possibly adaptive adversarial perturbations to the noise distribution within a prescribed ambiguity set. Two adversary models are considered: current-action-aware and current-action-unaware, leading to different dynamic programming equations. The paper also characterizes the optimal finite sample minimax rates for achieving uniform learning of the robust value function across continuum states under both adversary types. Furthermore, it demonstrates the applicability of the framework across various real-world settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us control systems that are affected by random events. We usually assume that these events happen randomly and independently, but in real-life situations, this might not be true. The paper proposes a new way to think about this problem by considering that someone or something might intentionally try to make the system behave differently than expected. There are two types of adversaries: one that knows our actions and one that doesn’t. The paper shows how to solve this problem using special algorithms and proves that its approach works well in different scenarios. |
