Provable Defense against Backdoor Policies in Reinforcement Learning
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We propose a provable defense mechanism against backdoor policies in reinforcement learning under subspace trigger assumption. A backdoor policy is a security threat where an adversary publishes a seemingly well-behaved policy which in fact allows hidden triggers. During deployment, the adversary can modify observed states in a particular way to trigger unexpected actions and harm the agent. We assume the agent does not have the resources to re-train a good policy. Instead, our defense mechanism sanitizes the backdoor policy by projecting observed states to a 'safe subspace', estimated from a small number of interactions with a clean (non-triggered) environment. Our sanitized policy achieves ϵ approximate optimality in the presence of triggers, provided the number of clean interactions is O(D/(1-γ)^4 ϵ^2) where γ is the discounting factor and D is the dimension of state space. Empirically, we show that our sanitization defense performs well on two Atari game environments.
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