When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning

Arahan Kujur

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arXiv · 2605.16312

When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning

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.

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Arahan Kujur. 2026-05-04. When Actions Disappear: Adversarial Action Removal in Self-Play Reinforcement Learning. https://arxiv.org/abs/2605.16312

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