Justin Fu | DeepAI

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∙ 12/21/2022

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

Imitation learning (IL) is a simple and powerful way to use high-quality...

0 Yiren Lu, et al. ∙

∙ 10/18/2022

Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving

We demonstrate the first large-scale application of model-based generati...

0 Eli Bronstein, et al. ∙

∙ 04/18/2022

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

Conventionally, generation of natural language for dialogue agents may b...

0 Siddharth Verma, et al. ∙

∙ 04/18/2022

Context-Aware Language Modeling for Goal-Oriented Dialogue Systems

Goal-oriented dialogue systems face a trade-off between fluent language ...

3 Charlie Snell, et al. ∙

∙ 03/30/2021

Benchmarks for Deep Off-Policy Evaluation

Off-policy evaluation (OPE) holds the promise of being able to leverage ...

13 Justin Fu, et al. ∙

∙ 02/16/2021

Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation

In this work we consider data-driven optimization problems where one mus...

9 Justin Fu, et al. ∙

∙ 05/04/2020

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

In this tutorial article, we aim to provide the reader with the conceptu...

29 Sergey Levine, et al. ∙

∙ 04/15/2020

D4RL: Datasets for Deep Data-Driven Reinforcement Learning

The offline reinforcement learning (RL) problem, also referred to as bat...

41 Justin Fu, et al. ∙

∙ 04/15/2020

Datasets for Data-Driven Reinforcement Learning

The offline reinforcement learning (RL) problem, also referred to as bat...

6 Justin Fu, et al. ∙

∙ 12/12/2019

Learning To Reach Goals Without Reinforcement Learning

Imitation learning algorithms provide a simple and straightforward appro...

0 Dibya Ghosh, et al. ∙

∙ 06/19/2019

When to Trust Your Model: Model-Based Policy Optimization

Designing effective model-based reinforcement learning algorithms is dif...

0 Michael Janner, et al. ∙

∙ 06/03/2019

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Off-policy reinforcement learning aims to leverage experience collected ...

7 Aviral Kumar, et al. ∙

∙ 02/26/2019

Diagnosing Bottlenecks in Deep Q-learning Algorithms

Q-learning methods represent a commonly used class of algorithms in rein...

0 Justin Fu, et al. ∙

∙ 02/20/2019

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

Reinforcement learning is a promising framework for solving control prob...

6 Justin Fu, et al. ∙

∙ 05/29/2018

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 ...

0 Justin Fu, et al. ∙

∙ 12/01/2016

Generalizing Skills with Semi-Supervised Reinforcement Learning

Deep reinforcement learning (RL) can acquire complex behaviors from low-...

0 Chelsea Finn, et al. ∙

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