title: Robust Reinforcement Learning via Adversarial Training with  Langevin Dynamics
abstract: We re-think the Two-Player Reinforcement Learning (RL) as an instance of a distribution sampling problem in infinite dimensions. Using the powerful Stochastic Gradient Langevin Dynamics, we propose a new two-player RL algorithm, which is a sampling variant of the two-player policy gradient method. Our new algorithm consistently outperforms existing baselines, in terms of generalization across differing training and testing conditions, on several MuJoCo environments.
authors: Huang Yu-Ting Parameswaran Kamalaruban Paul Rolland Ya-Ping Hsieh Volkan Cevher
hIndex: Volkan Cevher 49