When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning
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Computer Science > Machine Learning
Title:When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning
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
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