248 citations · 1.1k across the 25 of their papers we have counts for
37 papers
Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
Seyed Kamyar Seyed Ghasemipour, Shixiang Shane Gu, Ofir Nachum
Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of -functions can be leveraged as the primary so…
Chain of Thought Imitation with Procedure Cloning
Mengjiao Yang, Dale Schuurmans, Pieter Abbeel +1
Imitation learning aims to extract high-performance policies from logged demonstrations of expert behavior. It is common to frame imitation learning as a supervised learning proble…
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data
Mengjiao Yang, Sergey Levine, Ofir Nachum
The aim in imitation learning is to learn effective policies by utilizing near-optimal expert demonstrations. However, high-quality demonstrations from human experts can be expensi…
Policy Gradients Incorporating the Future
David Venuto, Elaine Lau, Doina Precup +1
Reasoning about the future -- understanding how decisions in the present time affect outcomes in the future -- is one of the central challenges for reinforcement learning (RL), esp…
Provable Representation Learning for Imitation with Contrastive Fourier Features
Ofir Nachum, Mengjiao Yang
In imitation learning, it is common to learn a behavior policy to match an unknown target policy via max-likelihood training on a collected set of target demonstrations. In this wo…
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Michael R. Zhang, Tom Le Paine, Ofir Nachum +4
Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action…