White-Box Adversarial Policies in Deep Reinforcement Learning
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Adversarial examples against AI systems pose both risks via malicious attacks and opportunities for improving robustness via adversarial training. In multiagent settings, adversarial policies can be developed by training an adversarial agent to minimize a victim agent's rewards. Prior work has studied black-box attacks where the adversary only sees the state observations and effectively treats the victim as any other part of the environment. In this work, we experiment with white-box adversarial policies to study whether an agent's internal state can offer useful information for other agents. We make three contributions. First, we introduce white-box adversarial policies in which an attacker can observe a victim's internal state at each timestep. Second, we demonstrate that white-box access to a victim makes for better attacks in two-agent environments, resulting in both faster initial learning and higher asymptotic performance against the victim. Third, we show that training against white-box adversarial policies can be used to make learners in single-agent environments more robust to domain shifts.
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