Related papers: Admissable: Training Reinforcement Learning Agents against Adversarial Missingness

Admissable: Training Reinforcement Learning Agents against Adversarial Missingness

URL: http://arxiv.org/abs/2609.15297v1
Date: Mon, 14 Sep 2026 09:48:06 GMT
Title: Admissable: Training Reinforcement Learning Agents against Adversarial Missingness
Abstract summary: We consider the challenge of adversarial feature missingness: a scenario in which an adversary occludes features from the agent's observation in order to reduce performance as much as possible.<n>We develop an adversarial training algorithm and show its effectiveness in increasing robustness against adversarial missingness on three MuJoCo benchmark environments.
Score: 6.917394101928202
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: In order to make Reinforcement Learning algorithms applicable in real world scenarios, safety must be ensured even under adverse operating conditions. In this work, we consider the challenge of adversarial feature missingness: a scenario in which an adversary occludes features from the agent's observation in order to reduce performance as much as possible. We formally define adversarial missingness for Reinforcement Learning and compare it to the related concepts of $\ell_\infty$-norm bounded adversarial perturbations and learning with missing data. We develop an adversarial training algorithm and show its effectiveness in increasing robustness against adversarial missingness on three MuJoCo benchmark environments. Compared to a baseline trained with random uniform missingness, our method achieves better robustness on all three tasks.

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