Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns. (arXiv:2109.07723v2 [cs.LG] UPDATED)

Recent studies demonstrated the vulnerability of control policies learned
through deep reinforcement learning against adversarial attacks, raising
concerns about the application of such models to risk-sensitive tasks such as
autonomous driving. Threat models for these demonstrations are limited to (1)
targeted attacks through real-time manipulation of the agent's observation, and
(2) untargeted attacks through manipulation of the physical environment. The
former assumes full access to the agent's states/observations at all times,
while the latter has no control over attack outcomes. …

arxiv autonomous autonomous driving deep rl rl