We present a method for evaluating the sensitivity of deep reinforcement learning (RL) policies. We also formulate a zero-sum dynamic game for designing robust deep reinforcement learning policies. Our approach mitigates the brittleness of policies when agents are trained in a simulated environment and are later exposed to the real world where it is hazardous to employ RL policies. This framework for training deep RL policies involve a zero-sum  dynamic game against an adversarial agent, where the goal is to drive the system dynamics to a saddle region. Using a variant of the guided policy search algorithm, our agent learns to adopt robust policies that require less samples for learning the dynamics and performs better than the GPS algorithm. Without loss of generality, we demonstrate that deep RL policies trained in this fashion will be maximally robust to a ``worst" possible adversarial disturbances.
This paper demonstrates how H-infinity control theory can help better design robust deep policies for robot motor taks Proposes to incorporate elements of robust control into guided policy research in order to devise a method that is resilient to perturbations and model mismatch. The paper presents a method for evaluating the sensitivity and robustness of deep RL policies, and proposes a dynamic game approach for learning robust policies.