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.
AUTHORS
Written by
William F. Whitney
Rajat Agarwal
Kyunghyun Cho
Abhinav Gupta
Publisher
International Conference on Learning Representations (ICLR)
Recent Publications
September 24, 2026
Reinforcement Learni9ng
MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning
Sourabh Kulkarni, Ksheeraj Sai Vepuri, Basar Demir, Jason Bohrer, Emily Shen, Jianfa Chen, Nan Jiang, Ankit Jain, Harihar Subramanyam, Mannat Singh, Chirag Nagpal
September 24, 2026
July 17, 2026
Conversational AI
Reinforcement Learni9ng
Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning
Zilin Xiao, Qi Ma, Jason Chen, Xintao Chen, Avinash Atreya, Hanjie Chen, Vicente Ordonez
July 17, 2026
July 13, 2026
S-EMBER: A Large-Scale Benchmark for Streaming Egocentric Memory Retrieval
Xiaodong Wang, Xuanyi Zhao, Pedro Rodriguez, Devendra Singh Sachan, Barlas Oguz, Seungwhan Moon, Shang-Wen Li, Gargi Ghosh, Xin Dong, Wen-Tau Yih
July 13, 2026
July 03, 2026
Human & Machine Intelligence
Robotics
Interpreting Physics in Video World Models
Sonia Joseph, Quentin Garrido, Randall Balestriero, Matthew Kowal, Thomas Fel, Shahab Bakhtiari, Blake Richards, Mike Rabbat
July 03, 2026
October 31, 2019
NLP
Facebook AI's WAT19 Myanmar-English Translation Task Submission
Peng-Jen Chen, Jiajun Shen, Matt Le, Vishrav Chaudhary, Ahmed El-Kishky, Guillaume Wenzek, Myle Ott, Marc’Aurelio Ranzato
October 31, 2019
October 27, 2019
Order-Aware Generative Modeling Using the 3D-Craft Dataset | Facebook AI Research
Zhuoyuan Chen, Demi Guo, Tong Xiao, Saining Xie, Xinlei Chen, Haonan Yu, Jonathan Gray, Kavya Srinet, Haoqi Fan, Jerry Ma, Charles R. Qi, Shubham Tulsiani, Arthur Szlam, Larry Zitnick
October 27, 2019
April 25, 2020
Energy-Based Models for Atomic-Resolution Protein Conformations | Facebook AI Research
Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, Alexander Rives
April 25, 2020
June 11, 2019
Computer Vision
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero | Facebook AI Research
Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, Larry Zitnick
June 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.
Meta © 2026
