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

In three linesStudy of adversarial action removal attacks in self-play reinforcement learning. An attacker selectively masks legal actions from the victim's action set. Experiments on poker (6 to 5,531 states) and two non-poker domains: learned masking causes substantially more damage than random masking, persists across Q-learning/PPO/NFSP/DQN, transfers between agents, and is amplified by self-play.

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