Finite-Time Convergence of Distributionally Robust Q-Learning with Linear Function Approximation
arXiv:2510.01721v3 Announce Type: replace Abstract: Distributionally robust reinforcement learning (DRRL) seeks policies that perform well when the deployment transition model differs from the nominal model generating the data. Most finite-sample guarantees for DRRL are tabular, model-based, rely on generative access, or obtain function-approximation guarantees only under additional structure, such as linear-transition models or restrictive discount-factor conditions. We study discounted model-free robust Q-learning under an $(s,a)$-rectangular chi-square uncertainty set, with linear approxima
This paper represents continued academic progress in the theoretical foundations of robust reinforcement learning, addressing a persistent challenge in deploying AI safely and reliably in uncertain real-world environments.
Improved theoretical guarantees for robust Q-learning directly contribute to more reliable and deployable AI systems, enhancing trustworthiness and reducing risks in critical applications for strategic decision-makers.
The ability to develop AI agents that perform reliably when deployment conditions differ from training models becomes more theoretically grounded and robust.
- · AI researchers
- · Robotics industry
- · Autonomous systems developers
- · AI systems with poor generalization
- · Brittle model-based deployment strategies
This research provides a stronger theoretical basis for developing more robust AI agents capable of operating effectively in uncertain real-world conditions.
It accelerates the development and adoption of autonomous systems in critical sectors by reducing the uncertainty associated with their performance in varied environments.
Increased reliability and trustworthiness of AI could lead to broader societal integration of autonomous decision-making systems, impacting industries from logistics to defense.
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