  Adversarial Examples (AEs) generated by perturbing original training examples
are useful in improving the robustness of Deep Learning (DL) based models. Most
prior works, generate AEs that are either unconscionable due to lexical errors
or semantically or functionally deviant from original examples. In this paper,
we present ReinforceBug, a reinforcement learning framework, that learns a
policy that is transferable on unseen datasets and generates utility-preserving
and transferable (on other models) AEs. Our results show that our method is on
average 10% more successful as compared to the state-of-the-art attack
TextFooler. Moreover, the target models have on average 73.64% confidence in
the wrong prediction, the generated AEs preserve the functional equivalence and
semantic similarity (83.38% ) to their original counterparts, and are
transferable on other models with an average success rate of 46%.
