538 citations · 812 across the 6 of their papers we have counts for
6 papers
Conservative Q-Learning for Offline Reinforcement Learning
Aviral Kumar, Aurick Zhou, George Tucker +1
Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise…
Reward-Conditioned Policies
Aviral Kumar, Xue Bin Peng, Sergey Levine
Reinforcement learning offers the promise of automating the acquisition of complex behavioral skills. However, compared to commonly used and well-understood supervised learning met…
Model Inversion Networks for Model-Based Optimization
Aviral Kumar, Sergey Levine
In this work, we aim to solve data-driven optimization problems, where the goal is to find an input that maximizes an unknown score function given access to a dataset of inputs wit…
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
Xue Bin Peng, Aviral Kumar, Grace Zhang +1
In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines. Our goal is an algorithm that…
Calibration of Encoder Decoder Models for Neural Machine Translation
Aviral Kumar, Sunita Sarawagi
We study the calibration of several state of the art neural machine translation(NMT) systems built on attention-based encoder-decoder models. For structured outputs like in NMT, ca…
Diagnosing Bottlenecks in Deep Q-learning Algorithms
Justin Fu, Aviral Kumar, Matthew Soh +1
Q-learning methods represent a commonly used class of algorithms in reinforcement learning: they are generally efficient and simple, and can be combined readily with function appro…