mlProhard
rush-hour
umdctf
Task: a remote autonomous-car controller exposes a fixed policy and lets us upload a tiny observation-perturbation network under a strict L2 budget. Solution: optimize a bounded attack MLP end-to-end through differentiable car rollouts against recorded remote timestep traces so the spoofed observations steer the car to the hidden CTF goal.
$ ls tags/ techniques/
pytorchwebsocketadversarial_examplesreinforcement_learningobservation_spoofingpolicy_networkdifferentiable_simulationcontrol_systems
observation_space_attackdifferentiable_rollout_optimizationtimestep_domain_randomizationsoftmin_objectiveteacher_policy_distillation
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