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2020 – today
- 2023
[i37]Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine:
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning. CoRR abs/2303.05479 (2023)- [i36]Qiyang Li, Aviral Kumar, Ilya Kostrikov, Sergey Levine:
Efficient Deep Reinforcement Learning Requires Regulating Overfitting. CoRR abs/2304.10466 (2023) - 2022
- [c29]Homer Walke, Jonathan Yang, Albert Yu, Aviral Kumar, Jedrzej Orbik, Avi Singh, Sergey Levine:
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning. CoRL 2022: 1652-1662 - [c28]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. ICLR 2022 - [c27]Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine:
Should I Run Offline Reinforcement Learning or Behavioral Cloning? ICLR 2022 - [c26]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization for Architecting Hardware Accelerators. ICLR 2022 - [c25]Brandon Trabucco, Xinyang Geng, Aviral Kumar, Sergey Levine:
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization. ICML 2022: 21658-21676 - [c24]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine:
How to Leverage Unlabeled Data in Offline Reinforcement Learning. ICML 2022: 25611-25635 - [c23]Han Qi, Yi Su, Aviral Kumar, Sergey Levine:
Data-Driven Offline Decision-Making via Invariant Representation Learning. NeurIPS 2022 - [c22]Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar:
DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning. NeurIPS 2022 - [c21]Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, Ed H. Chi:
Off-Policy Actor-critic for Recommender Systems. RecSys 2022: 338-349 - [i35]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine:
How to Leverage Unlabeled Data in Offline Reinforcement Learning. CoRR abs/2202.01741 (2022) - [i34]Brandon Trabucco, Xinyang Geng, Aviral Kumar, Sergey Levine:
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization. CoRR abs/2202.08450 (2022) - [i33]Aviral Kumar, Joey Hong, Anikait Singh, Sergey Levine:
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning? CoRR abs/2204.05618 (2022) - [i32]Homer Walke, Jonathan Yang, Albert Yu, Aviral Kumar, Jedrzej Orbik, Avi Singh, Sergey Levine:
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning. CoRR abs/2207.04703 (2022) - [i31]Aviral Kumar, Anikait Singh, Frederik Ebert, Yanlai Yang, Chelsea Finn, Sergey Levine:
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials. CoRR abs/2210.05178 (2022) - [i30]Anikait Singh, Aviral Kumar, Quan Vuong, Yevgen Chebotar, Sergey Levine:
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints. CoRR abs/2211.01052 (2022) - [i29]Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar:
Dual Generator Offline Reinforcement Learning. CoRR abs/2211.01471 (2022) - [i28]Han Qi, Yi Su, Aviral Kumar, Sergey Levine:
Data-Driven Offline Decision-Making via Invariant Representation Learning. CoRR abs/2211.11349 (2022) - [i27]Aviral Kumar, Rishabh Agarwal, Xinyang Geng, George Tucker, Sergey Levine:
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes. CoRR abs/2211.15144 (2022) - [i26]Joey Hong, Aviral Kumar, Sergey Levine:
Confidence-Conditioned Value Functions for Offline Reinforcement Learning. CoRR abs/2212.04607 (2022) - 2021
- [c20]Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, Sergey Levine:
A Workflow for Offline Model-Free Robotic Reinforcement Learning. CoRL 2021: 417-428 - [c19]Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum:
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. ICLR 2021 - [c18]Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg:
Conservative Safety Critics for Exploration. ICLR 2021 - [c17]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, Thomas Paine:
Benchmarks for Deep Off-Policy Evaluation. ICLR 2021 - [c16]Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine:
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. ICLR 2021 - [c15]Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine:
Conservative Objective Models for Effective Offline Model-Based Optimization. ICML 2021: 10358-10368 - [c14]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn:
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. NeurIPS 2021: 11501-11516 - [c13]Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine:
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. NeurIPS 2021: 25502-25515 - [c12]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. NeurIPS 2021: 28954-28967 - [i25]Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn:
COMBO: Conservative Offline Model-Based Policy Optimization. CoRR abs/2102.08363 (2021) - [i24]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:
Benchmarks for Deep Off-Policy Evaluation. CoRR abs/2103.16596 (2021) - [i23]Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine:
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. CoRR abs/2107.06277 (2021) - [i22]Brandon Trabucco, Aviral Kumar, Xinyang Geng, Sergey Levine:
Conservative Objective Models for Effective Offline Model-Based Optimization. CoRR abs/2107.06882 (2021) - [i21]Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn:
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. CoRR abs/2109.08128 (2021) - [i20]Aviral Kumar, Anikait Singh, Stephen Tian, Chelsea Finn, Sergey Levine:
A Workflow for Offline Model-Free Robotic Reinforcement Learning. CoRR abs/2109.10813 (2021) - [i19]Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine:
Data-Driven Offline Optimization For Architecting Hardware Accelerators. CoRR abs/2110.11346 (2021) - [i18]Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron C. Courville, George Tucker, Sergey Levine:
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization. CoRR abs/2112.04716 (2021) - 2020
- [c11]Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, Sergey Levine:
Chaining Behaviors from Data with Model-Free Reinforcement Learning. CoRL 2020: 2162-2177 - [c10]Aviral Kumar, Abhishek Gupta, Sergey Levine:
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction. NeurIPS 2020 - [c9]Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn:
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL. NeurIPS 2020 - [c8]Aviral Kumar, Sergey Levine:
Model Inversion Networks for Model-Based Optimization. NeurIPS 2020 - [c7]Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. NeurIPS 2020 - [i17]Aviral Kumar, Abhishek Gupta, Sergey Levine:
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction. CoRR abs/2003.07305 (2020) - [i16]Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, Sergey Levine:
D4RL: Datasets for Deep Data-Driven Reinforcement Learning. CoRR abs/2004.07219 (2020) - [i15]Sergey Levine, Aviral Kumar, George Tucker, Justin Fu:
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems. CoRR abs/2005.01643 (2020) - [i14]Aviral Kumar, Aurick Zhou, George Tucker, Sergey Levine:
Conservative Q-Learning for Offline Reinforcement Learning. CoRR abs/2006.04779 (2020) - [i13]Anurag Ajay, Aviral Kumar, Pulkit Agrawal, Sergey Levine, Ofir Nachum:
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning. CoRR abs/2010.13611 (2020) - [i12]Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn:
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL. CoRR abs/2010.14484 (2020) - [i11]Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg:
Conservative Safety Critics for Exploration. CoRR abs/2010.14497 (2020) - [i10]Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine:
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning. CoRR abs/2010.14498 (2020) - [i9]Avi Singh, Albert Yu, Jonathan Yang, Jesse Zhang, Aviral Kumar, Sergey Levine:
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning. CoRR abs/2010.14500 (2020)
2010 – 2019
- 2019
- [c6]Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine:
Diagnosing Bottlenecks in Deep Q-learning Algorithms. ICML 2019: 2021-2030 - [c5]Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, Sergey Levine:
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. NeurIPS 2019: 11761-11771 - [c4]Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky:
Graph Normalizing Flows. NeurIPS 2019: 13556-13566 - [i8]Justin Fu, Aviral Kumar, Matthew Soh, Sergey Levine:
Diagnosing Bottlenecks in Deep Q-learning Algorithms. CoRR abs/1902.10250 (2019) - [i7]Aviral Kumar, Sunita Sarawagi:
Calibration of Encoder Decoder Models for Neural Machine Translation. CoRR abs/1903.00802 (2019) - [i6]Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky:
Graph Normalizing Flows. CoRR abs/1905.13177 (2019) - [i5]Aviral Kumar, Justin Fu, George Tucker, Sergey Levine:
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction. CoRR abs/1906.00949 (2019) - [i4]Xue Bin Peng, Aviral Kumar, Grace Zhang, Sergey Levine:
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning. CoRR abs/1910.00177 (2019) - [i3]Aviral Kumar, Sergey Levine:
Model Inversion Networks for Model-Based Optimization. CoRR abs/1912.13464 (2019) - [i2]Aviral Kumar, Xue Bin Peng, Sergey Levine:
Reward-Conditioned Policies. CoRR abs/1912.13465 (2019) - 2018
- [c3]Aviral Kumar, Sunita Sarawagi, Ujjwal Jain:
Trainable Calibration Measures For Neural Networks From Kernel Mean Embeddings. ICML 2018: 2810-2819 - 2017
- [c2]Shankara Narayanan Krishna, Aviral Kumar, Fabio Somenzi, Behrouz Touri, Ashutosh Trivedi:
The Reach-Avoid Problem for Constant-Rate Multi-mode Systems. ATVA 2017: 463-479 - [c1]Stanley Bak, Sergiy Bogomolov
, Thomas A. Henzinger, Aviral Kumar:
Challenges and Tool Implementation of Hybrid Rapidly-Exploring Random Trees. NSV@CAV 2017: 83-89 - [i1]Shankara Narayanan Krishna, Aviral Kumar, Fabio Somenzi, Behrouz Touri, Ashutosh Trivedi:
The Reach-Avoid Problem for Constant-Rate Multi-Mode Systems. CoRR abs/1707.04151 (2017)
Coauthor Index
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last updated on 2023-05-12 20:42 CEST by the dblp team
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