An attack system for generating perturbations of input signals in a recurrent neural network (RNN)-based target system using a deep reinforcement learning agent for generating perturbations is described. The attack system trains the reinforcement learning agent to determine a magnitude of perturbations for attacking the RNN-based target system. A perturbed input sensor signal having the determined amplitude is generated and presented to the RNN-based target system such that the RNN-based target system produces a modified output in response to the perturbed input sensor signal. The system uses the altered output to identify a failure mode of the RNN-based target system.
Deep reinforcement learning-based method for generating signals in dark to deceive recurrent neural networks