mlsanityData to 14 Oct 2026

Admissable: Training Reinforcement Learning Agents against Adversarial Missingness

arXiv 2609.15297 ↗First posted 14 Sep 2026cs.LGcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

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 ℓ_∞-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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 23 references that are in the map. First posted on arXiv 14 Sep 2026.

Builds on

14 of 23 references.

References · 14

  1. 2013Intriguing properties of neural networkscited by 5,155
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2018Learning Latent Dynamics for Planning from Pixelscited by 1,264
  4. 2017Adversarial Attacks on Neural Network Policiescited by 320
  5. 2020Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observationscited by 238
  6. 2017Robust Deep Reinforcement Learning with Adversarial Attackscited by 154
  7. 2021Robust Reinforcement Learning on State Observations with Learned Optimal Adversarycited by 135
  8. 2020Learning to Walk in the Real World with Minimal Human Effortcited by 111
  9. 2021Policy Smoothing for Provably Robust Reinforcement Learningcited by 40
  10. 2022Controlling Commercial Cooling Systems Using Reinforcement Learningcited by 29
  11. 2022On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learningcited by 13
  12. 2023Reinforcement Learning Approaches for Traffic Signal Control under Missing Datacited by 13
2 more
  1. 2019Robust Reinforcement Learning in POMDPs with Incomplete and Noisy Observationscited by 10
  2. 2019Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcarecited by 2

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