学习无关变量以实现策略泛化

Sep, 2018

Learning Invariances for Policy Generalization

Remi Tachet des Combes, Philip Bachman, Harm van Seijen

TL;DR本文研究机器学习领域中的强化学习问题，主要关注于学习能够适应不同环境的策略，探讨数据增强、元学习和对抗训练三种可能的策略泛化方法，发现数据增强方法是有效的，并研究了元学习和对抗学习作为替代的任务不可知方法的潜力。

Abstract

While recent progress has spawned very powerful machine learning systems, those agents remain extremely specialized and fail to transfer the knowledge they gain to similar yet unseen tasks. In this paper, we study a simple →