790 citations · 2.7k across the 58 of their papers we have counts for
7 papers · 1 filter
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…
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Aviral Kumar, Justin Fu, George Tucker +1
Off-policy reinforcement learning aims to leverage experience collected from prior policies for sample-efficient learning. However, in practice, commonly used off-policy approximat…
Graph Normalizing Flows
Jenny Liu, Aviral Kumar, Jimmy Ba +2
We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to…
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…