39 citations · 234 across the 29 of their papers we have counts for
6 papers · 1 filter
Contrastive Difference Predictive Coding
Chongyi Zheng, Ruslan Salakhutdinov, Benjamin Eysenbach
Predicting and reasoning about the future lie at the heart of many time-series questions. For example, goal-conditioned reinforcement learning can be viewed as learning representat…
Contrastive Example-Based Control
Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov +4
While many real-world problems that might benefit from reinforcement learning, these problems rarely fit into the MDP mold: interacting with the environment is often expensive and…
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
Yongyuan Liang, Yanchao Sun, Ruijie Zheng +5
Deploying reinforcement learning (RL) systems requires robustness to uncertainty and model misspecification, yet prior robust RL methods typically only study noise introduced indep…
HIQL: Offline Goal-Conditioned RL with Latent States as Actions
Seohong Park, Dibya Ghosh, Benjamin Eysenbach +1
Unsupervised pre-training has recently become the bedrock for computer vision and natural language processing. In reinforcement learning (RL), goal-conditioned RL can potentially p…
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment
Tianwei Ni, Michel Ma, Benjamin Eysenbach +1
Reinforcement learning (RL) algorithms face two distinct challenges: learning effective representations of past and present observations, and determining how actions influence futu…
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
Chongyi Zheng, Benjamin Eysenbach, Homer Walke +4
Robotic systems that rely primarily on self-supervised learning have the potential to decrease the amount of human annotation and engineering effort required to learn control strat…