Wasserstein Robust Reinforcement Learning

Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar +4

Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}…