Abstract In this paper we propose a method to augment Reinforcement Learning agent with legibility. This is inspired by the literature in Explainable Planning and allows regularize agent’s policy after training, without requiring modify its learning algorithm. achieved evaluating how optimal may produce observations that would make an observer model infer wrong policy. our formulation, decision...