Timezone: »
Introduction
Aviral Kumar · George Tucker · Rishabh Agarwal
Author Information
Aviral Kumar (UC Berkeley)
George Tucker (Google Brain)
Rishabh Agarwal (Google Research, Brain Team)
My research work mainly revolves around deep reinforcement learning (RL), often with the goal of making RL methods suitable for real-world problems, and includes an outstanding paper award at NeurIPS.
More from the Same Authors
-
2021 Spotlight: Neural Additive Models: Interpretable Machine Learning with Neural Nets »
Rishabh Agarwal · Levi Melnick · Nicholas Frosst · Xuezhou Zhang · Ben Lengerich · Rich Caruana · Geoffrey Hinton -
2021 : Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning »
Tianhe Yu · Aviral Kumar · Yevgen Chebotar · Chelsea Finn · Sergey Levine · Karol Hausman -
2021 : Should I Run Offline Reinforcement Learning or Behavioral Cloning? »
Aviral Kumar · Joey Hong · Anikait Singh · Sergey Levine -
2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine -
2021 : Offline Policy Selection under Uncertainty »
Mengjiao (Sherry) Yang · Bo Dai · Ofir Nachum · George Tucker · Dale Schuurmans -
2021 : Behavior Predictive Representations for Generalization in Reinforcement Learning »
Siddhant Agarwal · Aaron Courville · Rishabh Agarwal -
2022 : A Novel Stochastic Gradient Descent Algorithm for LearningPrincipal Subspaces »
Charline Le Lan · Joshua Greaves · Jesse Farebrother · Mark Rowland · Fabian Pedregosa · Rishabh Agarwal · Marc Bellemare -
2022 : Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks »
Jesse Farebrother · Joshua Greaves · Rishabh Agarwal · Charline Le Lan · Ross Goroshin · Pablo Samuel Castro · Marc Bellemare -
2022 : Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes »
Aviral Kumar · Rishabh Agarwal · XINYANG GENG · George Tucker · Sergey Levine -
2022 : Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios »
Yiren Lu · Yiren Lu · Yiren Lu · Justin Fu · George Tucker · Xinlei Pan · Eli Bronstein · Rebecca Roelofs · Benjamin Sapp · Brandyn White · Aleksandra Faust · Shimon Whiteson · Dragomir Anguelov · Sergey Levine -
2022 : Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks »
Jesse Farebrother · Joshua Greaves · Rishabh Agarwal · Charline Le Lan · Ross Goroshin · Pablo Samuel Castro · Marc Bellemare -
2022 : Revisiting Bellman Errors for Offline Model Selection »
Joshua Zitovsky · Rishabh Agarwal · Daniel de Marchi · Michael Kosorok -
2022 : Revisiting Bellman Errors for Offline Model Selection »
Joshua Zitovsky · Daniel de Marchi · Rishabh Agarwal · Michael Kosorok -
2022 : Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks »
Jesse Farebrother · Joshua Greaves · Rishabh Agarwal · Charline Le Lan · Ross Goroshin · Pablo Samuel Castro · Marc Bellemare -
2022 : Investigating Multi-task Pretraining and Generalization in Reinforcement Learning »
Adrien Ali Taiga · Rishabh Agarwal · Jesse Farebrother · Aaron Courville · Marc Bellemare -
2022 : Offline Q-learning on Diverse Multi-Task Data Both Scales And Generalizes »
Aviral Kumar · Rishabh Agarwal · XINYANG GENG · George Tucker · Sergey Levine -
2022 : Democratizing RL Research by Reusing Prior Computation »
Rishabh Agarwal -
2022 Workshop: 3rd Offline Reinforcement Learning Workshop: Offline RL as a "Launchpad" »
Aviral Kumar · Rishabh Agarwal · Aravind Rajeswaran · Wenxuan Zhou · George Tucker · Doina Precup · Aviral Kumar -
2022 Poster: Oracle Inequalities for Model Selection in Offline Reinforcement Learning »
Jonathan N Lee · George Tucker · Ofir Nachum · Bo Dai · Emma Brunskill -
2022 Poster: Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress »
Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare -
2021 : Speaker Intro »
Aviral Kumar · George Tucker -
2021 : Speaker Intro »
Aviral Kumar · George Tucker -
2021 : Retrospective Panel »
Sergey Levine · Nando de Freitas · Emma Brunskill · Finale Doshi-Velez · Nan Jiang · Rishabh Agarwal -
2021 : Invited Speaker Panel »
Sham Kakade · Minmin Chen · Philip Thomas · Angela Schoellig · Barbara Engelhardt · Doina Precup · George Tucker -
2021 : Speaker Intro »
Rishabh Agarwal · Aviral Kumar -
2021 : Speaker Intro »
Rishabh Agarwal · Aviral Kumar -
2021 Workshop: Offline Reinforcement Learning »
Rishabh Agarwal · Aviral Kumar · George Tucker · Justin Fu · Nan Jiang · Doina Precup · Aviral Kumar -
2021 : Opening Remarks »
Rishabh Agarwal · Aviral Kumar -
2021 : Behavior Predictive Representations for Generalization in Reinforcement Learning »
Siddhant Agarwal · Aaron Courville · Rishabh Agarwal -
2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization Q&A »
Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine -
2021 : DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization »
Aviral Kumar · Rishabh Agarwal · Tengyu Ma · Aaron Courville · George Tucker · Sergey Levine -
2021 Poster: COMBO: Conservative Offline Model-Based Policy Optimization »
Tianhe Yu · Aviral Kumar · Rafael Rafailov · Aravind Rajeswaran · Sergey Levine · Chelsea Finn -
2021 Poster: Coupled Gradient Estimators for Discrete Latent Variables »
Zhe Dong · Andriy Mnih · George Tucker -
2021 Oral: Deep Reinforcement Learning at the Edge of the Statistical Precipice »
Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare -
2021 Poster: Neural Additive Models: Interpretable Machine Learning with Neural Nets »
Rishabh Agarwal · Levi Melnick · Nicholas Frosst · Xuezhou Zhang · Ben Lengerich · Rich Caruana · Geoffrey Hinton -
2021 Poster: Conservative Data Sharing for Multi-Task Offline Reinforcement Learning »
Tianhe Yu · Aviral Kumar · Yevgen Chebotar · Karol Hausman · Sergey Levine · Chelsea Finn -
2021 Poster: Deep Reinforcement Learning at the Edge of the Statistical Precipice »
Rishabh Agarwal · Max Schwarzer · Pablo Samuel Castro · Aaron Courville · Marc Bellemare -
2021 Poster: Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability »
Dibya Ghosh · Jad Rahme · Aviral Kumar · Amy Zhang · Ryan Adams · Sergey Levine -
2020 : Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization »
Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine -
2020 : Conservative Objective Models: A Simple Approach to Effective Model-Based Optimization »
Brandon Trabucco · Aviral Kumar · XINYANG GENG · Sergey Levine -
2020 : Contributed Talk #3: Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning »
Rishabh Agarwal · Marlos C. Machado · Pablo Samuel Castro · Marc Bellemare -
2020 : Panel »
Emma Brunskill · Nan Jiang · Nando de Freitas · Finale Doshi-Velez · Sergey Levine · John Langford · Lihong Li · George Tucker · Rishabh Agarwal · Aviral Kumar -
2020 Workshop: Offline Reinforcement Learning »
Aviral Kumar · Rishabh Agarwal · George Tucker · Lihong Li · Doina Precup · Aviral Kumar -
2020 Poster: RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning »
Caglar Gulcehre · Ziyu Wang · Alexander Novikov · Thomas Paine · Sergio Gómez · Konrad Zolna · Rishabh Agarwal · Josh Merel · Daniel Mankowitz · Cosmin Paduraru · Gabriel Dulac-Arnold · Jerry Li · Mohammad Norouzi · Matthew Hoffman · Nicolas Heess · Nando de Freitas -
2020 Poster: DisARM: An Antithetic Gradient Estimator for Binary Latent Variables »
Zhe Dong · Andriy Mnih · George Tucker -
2020 Spotlight: DisARM: An Antithetic Gradient Estimator for Binary Latent Variables »
Zhe Dong · Andriy Mnih · George Tucker -
2020 Poster: Conservative Q-Learning for Offline Reinforcement Learning »
Aviral Kumar · Aurick Zhou · George Tucker · Sergey Levine -
2020 Poster: One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL »
Saurabh Kumar · Aviral Kumar · Sergey Levine · Chelsea Finn -
2020 Tutorial: (Track3) Offline Reinforcement Learning: From Algorithm Design to Practical Applications »
Sergey Levine · Aviral Kumar -
2019 : Poster Session »
Matthia Sabatelli · Adam Stooke · Amir Abdi · Paulo Rauber · Leonard Adolphs · Ian Osband · Hardik Meisheri · Karol Kurach · Johannes Ackermann · Matt Benatan · GUO ZHANG · Chen Tessler · Dinghan Shen · Mikayel Samvelyan · Riashat Islam · Murtaza Dalal · Luke Harries · Andrey Kurenkov · Konrad Żołna · Sudeep Dasari · Kristian Hartikainen · Ofir Nachum · Kimin Lee · Markus Holzleitner · Vu Nguyen · Francis Song · Christopher Grimm · Felipe Leno da Silva · Yuping Luo · Yifan Wu · Alex Lee · Thomas Paine · Wei-Yang Qu · Daniel Graves · Yannis Flet-Berliac · Yunhao Tang · Suraj Nair · Matthew Hausknecht · Akhil Bagaria · Simon Schmitt · Bowen Baker · Paavo Parmas · Benjamin Eysenbach · Lisa Lee · Siyu Lin · Daniel Seita · Abhishek Gupta · Riley Simmons-Edler · Yijie Guo · Kevin Corder · Vikash Kumar · Scott Fujimoto · Adam Lerer · Ignasi Clavera Gilaberte · Nicholas Rhinehart · Ashvin Nair · Ge Yang · Lingxiao Wang · Sungryull Sohn · J. Fernando Hernandez-Garcia · Xian Yeow Lee · Rupesh Srivastava · Khimya Khetarpal · Chenjun Xiao · Luckeciano Carvalho Melo · Rishabh Agarwal · Tianhe Yu · Glen Berseth · Devendra Singh Chaplot · Jie Tang · Anirudh Srinivasan · Tharun Kumar Reddy Medini · Aaron Havens · Misha Laskin · Asier Mujika · Rohan Saphal · Joseph Marino · Alex Ray · Joshua Achiam · Ajay Mandlekar · Zhuang Liu · Danijar Hafner · Zhiwen Tang · Ted Xiao · Michael Walton · Jeff Druce · Ferran Alet · Zhang-Wei Hong · Stephanie Chan · Anusha Nagabandi · Hao Liu · Hao Sun · Ge Liu · Dinesh Jayaraman · John Co-Reyes · Sophia Sanborn -
2019 : Contributed Talks »
Rishabh Agarwal · Adam Gleave · Kimin Lee -
2019 : Poster Session »
Ahana Ghosh · Javad Shafiee · Akhilan Boopathy · Alex Tamkin · Theodoros Vasiloudis · Vedant Nanda · Ali Baheri · Paul Fieguth · Andrew Bennett · Guanya Shi · Hao Liu · Arushi Jain · Jacob Tyo · Benjie Wang · Boxiao Chen · Carroll Wainwright · Chandramouli Shama Sastry · Chao Tang · Daniel S. Brown · David Inouye · David Venuto · Dhruv Ramani · Dimitrios Diochnos · Divyam Madaan · Dmitrii Krashenikov · Joel Oren · Doyup Lee · Eleanor Quint · elmira amirloo · Matteo Pirotta · Gavin Hartnett · Geoffroy Dubourg-Felonneau · Gokul Swamy · Pin-Yu Chen · Ilija Bogunovic · Jason Carter · Javier Garcia-Barcos · Jeet Mohapatra · Jesse Zhang · Jian Qian · John Martin · Oliver Richter · Federico Zaiter · Tsui-Wei Weng · Karthik Abinav Sankararaman · Kyriakos Polymenakos · Lan Hoang · mahdieh abbasi · Marco Gallieri · Mathieu Seurin · Matteo Papini · Matteo Turchetta · Matthew Sotoudeh · Mehrdad Hosseinzadeh · Nathan Fulton · Masatoshi Uehara · Niranjani Prasad · Oana-Maria Camburu · Patrik Kolaric · Philipp Renz · Prateek Jaiswal · Reazul Hasan Russel · Riashat Islam · Rishabh Agarwal · Alexander Aldrick · Sachin Vernekar · Sahin Lale · Sai Kiran Narayanaswami · Samuel Daulton · Sanjam Garg · Sebastian East · Shun Zhang · Soheil Dsidbari · Justin Goodwin · Victoria Krakovna · Wenhao Luo · Wesley Chung · Yuanyuan Shi · Yuh-Shyang Wang · Hongwei Jin · Ziping Xu -
2019 Poster: Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction »
Aviral Kumar · Justin Fu · George Tucker · Sergey Levine -
2019 Poster: Graph Normalizing Flows »
Jenny Liu · Aviral Kumar · Jimmy Ba · Jamie Kiros · Kevin Swersky -
2019 Poster: Energy-Inspired Models: Learning with Sampler-Induced Distributions »
Dieterich Lawson · George Tucker · Bo Dai · Rajesh Ranganath -
2019 Poster: Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse »
James Lucas · George Tucker · Roger Grosse · Mohammad Norouzi -
2018 : Spotlights »
Guangneng Hu · Ke Li · Aviral Kumar · Phi Vu Tran · Samuel G. Fadel · Rita Kuznetsova · Bong-Nam Kang · Behrouz Haji Soleimani · Jinwon An · Nathan de Lara · Anjishnu Kumar · Tillman Weyde · Melanie Weber · Kristen Altenburger · Saeed Amizadeh · Xiaoran Xu · Yatin Nandwani · Yang Guo · Maria Pacheco · William Fedus · Guillaume Jaume · Yuka Yoneda · Yunpu Ma · Yunsheng Bai · Berk Kapicioglu · Maximilian Nickel · Fragkiskos Malliaros · Beier Zhu · Aleksandar Bojchevski · Joshua Joseph · Gemma Roig · Esma Balkir · Xander Steenbrugge -
2018 Poster: Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion »
Jacob Buckman · Danijar Hafner · George Tucker · Eugene Brevdo · Honglak Lee -
2018 Oral: Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion »
Jacob Buckman · Danijar Hafner · George Tucker · Eugene Brevdo · Honglak Lee -
2017 Poster: REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models »
George Tucker · Andriy Mnih · Chris J Maddison · John Lawson · Jascha Sohl-Dickstein -
2017 Oral: REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models »
George Tucker · Andriy Mnih · Chris J Maddison · John Lawson · Jascha Sohl-Dickstein -
2017 Poster: Filtering Variational Objectives »
Chris Maddison · John Lawson · George Tucker · Nicolas Heess · Mohammad Norouzi · Andriy Mnih · Arnaud Doucet · Yee Teh