2021-07-01-ball21a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment	Reinforcement learning from large-scale offline datasets provides us with the ability to learn policies without potentially unsafe or impractical exploration. Significant progress has been made in the past few years in dealing with the challenge of correcting for differing behavior between the data collection and learned policies. However, little attention has been paid to potentially changing dynamics when transferring a policy to the online setting, where performance can be up to 90% reduced for existing methods. In this paper we address this problem with Augmented World Models (AugWM). We augment a learned dynamics model with simple transformations that seek to capture potential changes in physical properties of the robot, leading to more robust policies. We not only train our policy in this new setting, but also provide it with the sampled augmentation as a context, allowing it to adapt to changes in the environment. At test time we learn the context in a self-supervised fashion by approximating the augmentation which corresponds to the new environment. We rigorously evaluate our approach on over 100 different changed dynamics settings, and show that this simple approach can significantly improve the zero-shot generalization of a recent state-of-the-art baseline, often achieving successful policies where the baseline fails.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	ball21a	0	Augmented World Models Facilitate Zero-Shot Dynamics Generalization From a Single Offline Environment	619	629	619-629	619	false	Ball, Philip J and Lu, Cong and Parker-Holder, Jack and Roberts, Stephen	given family Philip J Ball given family Cong Lu given family Jack Parker-Holder given family Stephen Roberts	2021-07-01		Proceedings of the 38th International Conference on Machine Learning	139	inproceedings	date-parts 2021 7 1	http://proceedings.mlr.press/v139/ball21a/ball21a.pdf	label link Supplementary PDF http://proceedings.mlr.press/v139/ball21a/ball21a-supp.pdf