Justin Fu

According to our database, Justin Fu authored at least 25 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research.

Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, John D. Co-Reyes, Rishabh Agarwal, Rebecca Roelofs, Yao Lu, Nico Montali, Paul Mougin, Zoey Yang, Brandyn White, Aleksandra Faust, Rowan McAllister, Dragomir Anguelov, Benjamin Sapp

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios.

Yiren Lu, Justin Fu, George Tucker, Xinlei Pan, Eli Bronstein, Rebecca Roelofs, Benjamin Sapp, Brandyn White, Aleksandra Faust, Shimon Whiteson, Dragomir Anguelov, Sergey Levine

IROS, 2023

2022

Context-Aware Language Modeling for Goal-Oriented Dialogue Systems.

Charlie Snell, Mengjiao Yang, Justin Fu, Yi Su, Sergey Levine

CoRR, 2022

CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning.

Siddharth Verma, Justin Fu, Mengjiao Yang, Sergey Levine

CoRR, 2022

CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning.

Siddharth Verma, Justin Fu, Sherry Yang, Sergey Levine

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

Context-Aware Language Modeling for Goal-Oriented Dialogue Systems.

Charlie Snell, Sherry Yang, Justin Fu, Yi Su, Sergey Levine

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022

Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving.

Eli Bronstein, Mark Palatucci, Dominik Notz, Brandyn White, Alex Kuefler, Yiren Lu, Supratik Paul, Payam Nikdel, Paul Mougin, Hongge Chen, Justin Fu, Austin Abrams, Punit Shah, Evan Racah, Benjamin Frenkel, Shimon Whiteson, Dragomir Anguelov

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

2021

Evaluating Strategic Structures in Multi-Agent Inverse Reinforcement Learning.

Justin Fu, Andrea Tacchetti, Julien Pérolat, Yoram Bachrach

J. Artif. Intell. Res., 2021

Learning to Reach Goals via Iterated Supervised Learning.

Dibya Ghosh, Abhishek Gupta, Ashwin Reddy, Justin Fu, Coline Manon Devin, Benjamin Eysenbach, Sergey Levine

Proceedings of the 9th International Conference on Learning Representations, 2021

Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation.

Justin Fu, Sergey Levine

Proceedings of the 9th International Conference on Learning Representations, 2021

Benchmarks for Deep Off-Policy Evaluation.

Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

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

Sergey Levine, Aviral Kumar, George Tucker, Justin Fu

CoRR, 2020

D4RL: Datasets for Deep Data-Driven Reinforcement Learning.

Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine

CoRR, 2020

2019

Learning To Reach Goals Without Reinforcement Learning.

Dibya Ghosh, Abhishek Gupta, Justin Fu, Ashwin Reddy, Coline Devin, Benjamin Eysenbach, Sergey Levine

CoRR, 2019

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction.

Aviral Kumar, Justin Fu, George Tucker, Sergey Levine

CoRR, 2019

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction.

Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

When to Trust Your Model: Model-Based Policy Optimization.

Michael Janner, Justin Fu, Marvin Zhang, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Diagnosing Bottlenecks in Deep Q-learning Algorithms.

Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Justin Fu, Anoop Korattikara, Sergey Levine, Sergio Guadarrama

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

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

Justin Fu, Avi Singh, Dibya Ghosh, Larry Yang, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Robust Rewards with Adverserial Inverse Reinforcement Learning.

Justin Fu, Katie Luo, Sergey Levine

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Learning Robust Rewards with Adversarial Inverse Reinforcement Learning.

Justin Fu, Katie Luo, Sergey Levine

CoRR, 2017

EX2: Exploration with Exemplar Models for Deep Reinforcement Learning.

Justin Fu, John D. Co-Reyes, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Generalizing Skills with Semi-Supervised Reinforcement Learning.

Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

One-shot learning of manipulation skills with online dynamics adaptation and neural network priors.

Justin Fu, Sergey Levine, Pieter Abbeel

Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016