  Reinforcement learning has achieved remarkable performance in a wide range of
tasks these days. Nevertheless, some unsolved problems limit its applications
in real-world control. One of them is model misspecification, a situation where
an agent is trained and deployed in environments with different transition
dynamics. We propose an novel framework that utilize history trajectory and
Partial Observable Markov Decision Process Modeling to deal with this dilemma.
Additionally, we put forward an efficient adversarial attack method to assist
robust training. Our experiments in four gym domains validate the effectiveness
of our framework.
