Learning in MDPs with highly complex state representations is currently possible due to multiple advancements in reinforcement learning algorithm design. However, this incline in complexity, and furthermore the increase in the dimensions of the observation came at the cost of volatility that can be taken advantage of (i.e. moving along worst-case directions in the observation space). To solve this policy instability problem we propose a novel method to ascertain the presence of these non-robust…