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Reinforcement learning allows machines to learn from their own experience.Nowadays, it is used in safety-critical applications, such as autonomousdriving, despite being vulnerable to attacks carefully crafted to eitherprevent that the reinforcement learning algorithm learns an effective andreliable policy, or to induce the trained agent to make a wrong decision. Theliterature about the security of reinforcement learning is rapidly growing, andsome surveys have been proposed to shed light on this field. However, theircategorizations are insufficient for choosing an appropriate defense given thekind of system at hand. In our survey, we do not only overcome this limitationby considering a different perspective, but we also discuss the applicabilityof state-of-the-art attacks and defenses when reinforcement learning algorithmsare used in the context of autonomous driving.

A Survey on Reinforcement Learning Security with Application to Autonomous Driving