Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

02/14/2020 ∙ by Parameswaran Kamalaruban, et al. ∙ 0

We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents. Leveraging the powerful Stochastic Gradient Langevin Dynamics, we present a novel, scalable two-player RL algorithm, which is a sampling variant of the two-player policy gradient method. Our algorithm consistently outperforms existing baselines, in terms of generalization across different training and testing conditions, on several MuJoCo environments. Our experiments also show that, even for objective functions that entirely ignore potential environmental shifts, our sampling approach remains highly robust in comparison to standard RL algorithms.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 18

page 19

page 20

page 21

page 24

page 26

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.