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Ofir Nachum — most-cited papers & profile · Reinforcement Learning
← authors
·
overview
Ofir Nachum
53
papers ·
2190
citations
Google Scholar ↗
Semantic Scholar ↗
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Most-cited papers
D4RL: Datasets for Deep Data-Driven Reinforcement Learning
2020 · 332 citations
Data-Efficient Hierarchical Reinforcement Learning
2018 · 265 citations
Behavior Regularized Offline Reinforcement Learning
2019 · 249 citations
Bridging the Gap Between Value and Policy Based Reinforcement Learning
2017 · 229 citations
Lyapunov-based Safe Policy Optimization for Continuous Control
2019 · 154 citations
DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections
2019 · 90 citations
AlgaeDICE: Policy Gradient from Arbitrary Experience
2019 · 82 citations
A Lyapunov-based Approach to Safe Reinforcement Learning
2018 · 78 citations
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
2019 · 67 citations
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
2018 · 63 citations
Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?
2019 · 51 citations
Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization
2020 · 50 citations
Offline Reinforcement Learning with Fisher Divergence Critic Regularization
2021 · 39 citations
Multi-Agent Manipulation via Locomotion using Hierarchical Sim2Real
2019 · 37 citations
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control
2017 · 31 citations
Top co-authors
George Tucker
· 9
Sergey Levine
· 9
Dale Schuurmans
· 8
Bo Dai
· 6
Yinlam Chow
· 6
Aviral Kumar
· 4
Jonathan Tompson
· 4
Aldo Pacchiano
· 3
Chelsea Finn
· 3
Cosmin Paduraru
· 3
Hiroki Furuta
· 3
Honglak Lee
· 3
Topics
Offline RL
Value-Based
Model-Based RL
Policy Gradient
Safe RL
Meta-RL
Exploration
Game AI
Multi-Agent