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

This research investigates adversarial action masking in self-play reinforcement learning, where an attacker selectively removes legal actions from a victim's action set. The study found that learned masking causes significantly more damage than random masking or perturbation baselines, highlighting action availability as a critical robustness surface in self-play RL.