Adversarial Policy Learning in Two-player Competitive Games
Wenbo Guo,u00a0Xian Wu,u00a0Sui Huang,u00a0Xinyu Xing
In a two-player deep reinforcement learning task, recent work shows an attacker could learn an adversarial policy that triggers a target agent to perform poorly and even react in an undesired way. However, its efficacy heavily relies upon the zero-sum assumption made in the two-player game. In this work, we propose a new adversarial learning algorithm. It addresses the problem by resetting the optimization goal in the learning process and designing a new surrogate optimization function. Our experiments show that our method significantly improves adversarial agentsu2019 exploitability compared with the state-of-art attack. Besides, we also discover that our method could augment an agent with the ability to abuse the target gameu2019s unfairness. Finally, we show that agents adversarially re-trained against our adversarial agents could obtain stronger adversary-resistance.
