[2605.16312] When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning
[Submitted on 4 May 2026]
Title:When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning
View a PDF of the paper titled When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning, by Arahan Kujur
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Abstract:We study adversarial action masking in self-play reinforcement learning: an attacker selectively removes legal actions from a victim's action set. Unlike observation or action perturbations, removal eliminates decision options before the agent acts. Across poker games scaling from 6 to 5,531 information states and two non-poker domains, learned masking causes substantially more damage than random masking and learned perturbation baselines. The attack persists across Q-learning, PPO, NFSP, neural NFSP, and DQN victims; transfers across agents; is amplified by self-play; and shows no recovery under extended masked training. Mechanistically, the adversary targets high-value decision points, captured by reach-weighted contingent action capacity (CAC$_w$) and a value-weighted refinement CAC$_v$. These results identify action availability as a distinct robustness surface in self-play RL.
Comments: 17 pages, 2 figures, 18 tables
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
ACM classes: I.2.6; I.2.11
Cite as: arXiv:2605.16312 [cs.LG]
(or arXiv:2605.16312v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.16312
arXiv-issued DOI via DataCite
Submission history
From: Arahan Kujur [view email] [v1] Mon, 4 May 2026 14:05:39 UTC (34 KB)
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