Research

Dynamics-aware Embeddings

April 25, 2020

Abstract

In this paper we consider self-supervised representation learning to improve sample efficiency in reinforcement learning (RL). We propose a forward prediction objective for simultaneously learning embeddings of states and action sequences. These embeddings capture the structure of the environment’s dynamics, enabling efficient policy learning. We demonstrate that our action embeddings alone improve the sample efficiency and peak performance of model-free RL on control from low-dimensional states. By combining state and action embeddings, we achieve efficient learning of high-quality policies on goal-conditioned continuous control from pixel observations in only 1-2 million environment steps.

Download the Paper

AUTHORS

Written by

William F. Whitney

Rajat Agarwal

Kyunghyun Cho

Abhinav Gupta

Publisher

International Conference on Learning Representations (ICLR)

Recent Publications

April 17, 2025

Human & Machine Intelligence

Conversational AI

Collaborative Reasoner: Self-improving Social Agents with Synthetic Conversations

Ansong Ni, Ruta Desai, Yang Li, Xinjie Lei, Dong Wang, Ramya Raghavendra, Gargi Ghosh, Daniel Li (FAIR), Asli Celikyilmaz

April 17, 2025

April 16, 2025

Robotics

Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D

Paul McVay, Sergio Arnaud, Ada Martin, Arjun Majumdar, Krishna Murthy Jatavallabhula, Phillip Thomas, Ruslan Partsey, Daniel Dugas, Abha Gejji, Alexander Sax, Vincent-Pierre Berges, Mikael Henaff, Ayush Jain, Ang Cao, Ishita Prasad, Mrinal Kalakrishnan, Mike Rabbat, Nicolas Ballas, Mido Assran, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier

April 16, 2025

April 14, 2025

Graphics

Autoregressive Distillation of Diffusion Transformers

Yeongmin Kim, Sotiris Anagnostidis, Yuming Du, Edgar Schoenfeld, Jonas Kohler, Markos Georgopoulos, Albert Pumarola, Ali Thabet, Artsiom Sanakoyeu

April 14, 2025

March 24, 2025

Integrity

Data Taggants: Dataset Ownership Verification Via Harmless Targeted Data Poisoning

Wassim (Wes) Bouaziz, Nicolas Usunier, El Mahdi El Mhamdi

March 24, 2025

April 08, 2021

Responsible AI

Integrity

Towards measuring fairness in AI: the Casual Conversations dataset

Caner Hazirbas, Joanna Bitton, Brian Dolhansky, Jacqueline Pan, Albert Gordo, Cristian Canton Ferrer

April 08, 2021

April 30, 2018

The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings | Facebook AI Research

Tomer Galanti, Lior Wolf, Sagie Benaim

April 30, 2018

April 30, 2018

Computer Vision

NAM – Unsupervised Cross-Domain Image Mapping without Cycles or GANs | Facebook AI Research

Yedid Hoshen, Lior Wolf

April 30, 2018

December 11, 2019

Speech & Audio

Computer Vision

Hyper-Graph-Network Decoders for Block Codes | Facebook AI Research

Eliya Nachmani, Lior Wolf

December 11, 2019

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.