Justin Fu

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Google ∙ berkeley college

Benchmarks for Deep Off-Policy Evaluation

Off-policy evaluation (OPE) holds the promise of being able to leverage ...
03/30/2021 ∙ by Justin Fu, et al. ∙ 13 ∙ share

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Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation

In this work we consider data-driven optimization problems where one mus...
02/16/2021 ∙ by Justin Fu, et al. ∙ 9 ∙ share

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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

In this tutorial article, we aim to provide the reader with the conceptu...
05/04/2020 ∙ by Sergey Levine, et al. ∙ 29 ∙ share

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D4RL: Datasets for Deep Data-Driven Reinforcement Learning

The offline reinforcement learning (RL) problem, also referred to as bat...
04/15/2020 ∙ by Justin Fu, et al. ∙ 41 ∙ share

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Datasets for Data-Driven Reinforcement Learning

The offline reinforcement learning (RL) problem, also referred to as bat...
04/15/2020 ∙ by Justin Fu, et al. ∙ 6 ∙ share

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Learning To Reach Goals Without Reinforcement Learning

Imitation learning algorithms provide a simple and straightforward appro...
12/12/2019 ∙ by Dibya Ghosh, et al. ∙ 0 ∙ share

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When to Trust Your Model: Model-Based Policy Optimization

Designing effective model-based reinforcement learning algorithms is dif...
06/19/2019 ∙ by Michael Janner, et al. ∙ 0 ∙ share

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Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Off-policy reinforcement learning aims to leverage experience collected ...
06/03/2019 ∙ by Aviral Kumar, et al. ∙ 7 ∙ share

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Diagnosing Bottlenecks in Deep Q-learning Algorithms

Q-learning methods represent a commonly used class of algorithms in rein...
02/26/2019 ∙ by Justin Fu, et al. ∙ 0 ∙ share

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From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

Reinforcement learning is a promising framework for solving control prob...
02/20/2019 ∙ by Justin Fu, et al. ∙ 6 ∙ share

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Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition

The design of a reward function often poses a major practical challenge ...
05/29/2018 ∙ by Justin Fu, et al. ∙ 0 ∙ share

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Generalizing Skills with Semi-Supervised Reinforcement Learning

Deep reinforcement learning (RL) can acquire complex behaviors from low-...
12/01/2016 ∙ by Chelsea Finn, et al. ∙ 0 ∙ share

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Justin Fu

Featured Co-authors

Sergey Levine

Pieter Abbeel

Chelsea Finn

Abhishek Gupta

Mohammad Norouzi

Ziyu Wang

Ofir Nachum

George Tucker

Yutian Chen

Benjamin Eysenbach

Aviral Kumar

Benchmarks for Deep Off-Policy Evaluation

Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation

Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

Datasets for Data-Driven Reinforcement Learning

Learning To Reach Goals Without Reinforcement Learning

When to Trust Your Model: Model-Based Policy Optimization

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Diagnosing Bottlenecks in Deep Q-learning Algorithms

From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition

Generalizing Skills with Semi-Supervised Reinforcement Learning