Doubly Robust Augmented Transfer for Meta-Reinforcement Learning

In this paper, we propose a doubly robust augmented transfer (DRaT) approach, aiming at addressing the more general sparse reward meta-RL scenario with both dynamics mismatches and varying reward functions across tasks.