Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process (MDP) is stationary. However, in many practical real-world applications, this assumption is clearly violated. We discuss how current methods can have inherent limitations for non-stationary MDPs, and therefore searching a policy that is good for the future, unknown MDP, requires rethinking the optimization paradigm. To…