### The Importance Of Sampling InMeta-Reinforcement Learning

 Authors: Bradly Stadie Vector Institute Ge Yang Berkeley Rein Houthooft Happy Elements Peter Chen covariant.ai Yan Duan UC Berkeley Yuhuai Wu University of Toronto Pieter Abbeel UC Berkeley / Gradescope / Covariant Ilya Sutskever OpenAI

Introduction:

The authors interpret meta-reinforcement learning as the problem of learning how to quickly find a good sampling distribution in a new environment.

Abstract:

We interpret meta-reinforcement learning as the problem of learning how to quickly find a good sampling distribution in a new environment. This interpretation leads to the development of two new meta-reinforcement learning algorithms: E-MAML and E-$\text{RL}^2$. Results are presented on a new environment we call `Krazy World': a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning. Further results are presented on a set of maze environments. We show E-MAML and E-$\text{RL}^2$ deliver better performance than baseline algorithms on both tasks.