Publication

Learning Invariant Representations for Reinforcement Learning without Reconstruction

International Conference on Learning Representations (ICLR)


Abstract

We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that provide for effective downstream control and invariance to task-irrelevant details. Bisimulation metrics quantify behavioral similarity between states in continuous MDPs, which we propose using to learn robust latent representations which encode only the task-relevant information from observations. Our method trains encoders such that distances in latent space equal bisimulation distances in state space. We demonstrate the effectiveness of our method at disregarding task-irrelevant information using modified visual MuJoCo tasks, where the background is replaced with moving distractors and natural videos, while achieving SOTA performance. We also test a first-person highway driving task where our method learns invariance to clouds, weather, and time of day. Finally, we provide generalization results drawn from properties of bisimulation metrics, and links to causal inference.

Related Publications

All Publications

EACL - April 18, 2021

Co-evolution of language and agents in referential games

Gautier Dagan, Dieuwke Hupkes, Elia Bruni

PPSN - September 2, 2020

Variance Reduction for Better Sampling in Continuous Domains

Laurent Meunier, Carola Doerr, Jeremy Rapin, Olivier Teytaud

ACL - May 2, 2021

MLQA: Evaluating Cross-lingual Extractive Question Answering

Patrick Lewis, Barlas O─čuz, Ruty Rinott, Sebastian Riedel, Holger Schwenk

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy