Robust Reinforcement Learning via Adversarial Kernel Approximation

Robust Markov Decision Processes (RMDPs) provide a framework for sequential decision-making that is robust to perturbations on the transition kernel. However, robust reinforcement learning (RL) approaches in RMDPs do not scale well to realistic online settings with high-dimensional domains. By characterizing the adversarial kernel in RMDPs, we propose a novel approach for online robust RL that approximates the adversarial kernel and uses a standard (non-robust) RL algorithm to learn a robust policy. Notably, our approach can be applied on top of any underlying RL algorithm, enabling easy scaling to high-dimensional domains. Experiments in classic control tasks, MinAtar and DeepMind Control Suite demonstrate the effectiveness and the applicability of our method.