  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 $\epsilon$ approximate optimality in the presence of triggers,
provided the number of clean interactions is $O\left(\frac{D}{(1-\gamma)^4
\epsilon^2}\right)$ where $\gamma$ 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.
