324 citations · 2.8k across the 66 of their papers we have counts for
10 papers · 1 filter
Imitation Learning via Off-Policy Distribution Matching
Ilya Kostrikov, Ofir Nachum, Jonathan Tompson
When performing imitation learning from expert demonstrations, distribution matching is a popular approach, in which one alternates between estimating distribution ratios and then…
AlgaeDICE: Policy Gradient from Arbitrary Experience
Ofir Nachum, Bo Dai, Ilya Kostrikov +3
In many real-world applications of reinforcement learning (RL), interactions with the environment are limited due to cost or feasibility. This presents a challenge to traditional R…
Behavior Regularized Offline Reinforcement Learning
Yifan Wu, George Tucker, Ofir Nachum
In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment. However in many real-world applications, access to the en…
Group-based Fair Learning Leads to Counter-intuitive Predictions
Ofir Nachum, Heinrich Jiang
A number of machine learning (ML) methods have been proposed recently to maximize model predictive accuracy while enforcing notions of group parity or fairness across sub-populatio…
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
Ofir Nachum, Haoran Tang, Xingyu Lu +3
Hierarchical reinforcement learning has demonstrated significant success at solving difficult reinforcement learning (RL) tasks. Previous works have motivated the use of hierarchy…
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
Ofir Nachum, Michael Ahn, Hugo Ponte +2
Manipulation and locomotion are closely related problems that are often studied in isolation. In this work, we study the problem of coordinating multiple mobile agents to exhibit m…
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman +2
Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process…
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
Ofir Nachum, Yinlam Chow, Bo Dai +1
In many real-world reinforcement learning applications, access to the environment is limited to a fixed dataset, instead of direct (online) interaction with the environment. When u…
Lyapunov-based Safe Policy Optimization for Continuous Control
Yinlam Chow, Ofir Nachum, Aleksandra Faust +2
We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that do n…
Identifying and Correcting Label Bias in Machine Learning
Heinrich Jiang, Ofir Nachum
Datasets often contain biases which unfairly disadvantage certain groups, and classifiers trained on such datasets can inherit these biases. In this paper, we provide a mathematica…