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A backdoor attack allows a malicious user to manipulate the environment orcorrupt the training data, thus inserting a backdoor into the trained agent.Such attacks compromise the RL system's reliability, leading to potentiallycatastrophic results in various key fields. In contrast, relatively limitedresearch has investigated effective defenses against backdoor attacks in RL.This paper proposes the Recovery Triggered States (RTS) method, a novelapproach that effectively protects the victim agents from backdoor attacks. RTSinvolves building a surrogate network to approximate the dynamics model.Developers can then recover the environment from the triggered state to a cleanstate, thereby preventing attackers from activating backdoors hidden in theagent by presenting the trigger. When training the surrogate to predict states,we incorporate agent action information to reduce the discrepancy between theactions taken by the agent on predicted states and the actions taken on realstates. RTS is the first approach to defend against backdoor attacks in asingle-agent setting. Our results show that using RTS, the cumulative rewardonly decreased by 1.41% under the backdoor attack.

Recover Triggered States: Protect Model Against Backdoor Attack in Reinforcement Learning