Чланци са овлашћеним јавним приступом - Aviral KumarСазнајте више
Доступно негде: 22
Conservative q-learning for offline reinforcement learning
A Kumar, A Zhou, G Tucker, S Levine
Advances in Neural Information Processing Systems 33, 1179-1191, 2020
Овлашћења: US Department of Defense
D4rl: Datasets for deep data-driven reinforcement learning
J Fu, A Kumar, O Nachum, G Tucker, S Levine
arXiv preprint arXiv:2004.07219, 2020
Овлашћења: US Department of Defense
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
A Kumar, J Fu, G Tucker, S Levine
NeuRIPS 2019, arXiv:1906.00949, 2019
Овлашћења: US National Science Foundation, US Department of Defense
Combo: Conservative offline model-based policy optimization
T Yu, A Kumar, R Rafailov, A Rajeswaran, S Levine, C Finn
Advances in neural information processing systems 34, 28954-28967, 2021
Овлашћења: US Department of Defense
Graph Normalizing Flows
J Liu, A Kumar, J Ba, J Kiros, K Swersky
NeurIPS 2019, arxiv:1905.13177, 2019
Овлашћења: US National Institutes of Health, National Natural Science Foundation of …
Diagnosing Bottlenecks in Deep Q-learning Algorithms
J Fu, A Kumar, M Soh, S Levine
International Conference on Machine Learning (ICML) 2019, https://arxiv.org …, 2019
Овлашћења: US National Science Foundation, US Department of Defense
When should we prefer offline reinforcement learning over behavioral cloning?
A Kumar, J Hong, A Singh, S Levine
arXiv preprint arXiv:2204.05618, 2022
Овлашћења: US Department of Defense
Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability
D Ghosh, J Rahme, A Kumar, A Zhang, RP Adams, S Levine
Advances in neural information processing systems 34, 25502-25515, 2021
Овлашћења: US National Science Foundation, US Department of Defense
Discor: Corrective feedback in reinforcement learning via distribution correction
A Kumar, A Gupta, S Levine
Advances in Neural Information Processing Systems 33, 18560-18572, 2020
Овлашћења: US National Science Foundation, US Department of Defense
Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning
M Nakamoto, S Zhai, A Singh, M Sobol Mark, Y Ma, C Finn, A Kumar, ...
Advances in Neural Information Processing Systems 36, 2024
Овлашћења: US National Science Foundation, US Department of Defense
One solution is not all you need: Few-shot extrapolation via structured maxent rl
S Kumar, A Kumar, S Levine, C Finn
Advances in Neural Information Processing Systems 33, 8198-8210, 2020
Овлашћења: US National Science Foundation, US Department of Defense
Design-bench: Benchmarks for data-driven offline model-based optimization
B Trabucco, X Geng, A Kumar, S Levine
International Conference on Machine Learning, 21658-21676, 2022
Овлашћења: US Department of Defense
Conservative objective models for effective offline model-based optimization
B Trabucco, A Kumar, X Geng, S Levine
International Conference on Machine Learning, 10358-10368, 2021
Овлашћења: US National Science Foundation, US Department of Defense
Model inversion networks for model-based optimization
A Kumar, S Levine
Advances in neural information processing systems 33, 5126-5137, 2020
Овлашћења: US National Science Foundation, US Department of Defense
Conservative data sharing for multi-task offline reinforcement learning
T Yu, A Kumar, Y Chebotar, K Hausman, S Levine, C Finn
Advances in Neural Information Processing Systems 34, 11501-11516, 2021
Овлашћења: US Department of Defense
How to leverage unlabeled data in offline reinforcement learning
T Yu, A Kumar, Y Chebotar, K Hausman, C Finn, S Levine
International Conference on Machine Learning, 25611-25635, 2022
Овлашћења: US Department of Defense
Don’t start from scratch: Leveraging prior data to automate robotic reinforcement learning
HR Walke, JH Yang, A Yu, A Kumar, J Orbik, A Singh, S Levine
Conference on Robot Learning, 1652-1662, 2023
Овлашћења: US National Science Foundation, US Department of Defense
Data-driven offline decision-making via invariant representation learning
H Qi, Y Su, A Kumar, S Levine
Advances in Neural Information Processing Systems 35, 13226-13237, 2022
Овлашћења: US Department of Defense
Action-quantized offline reinforcement learning for robotic skill learning
J Luo, P Dong, J Wu, A Kumar, X Geng, S Levine
Conference on Robot Learning, 1348-1361, 2023
Овлашћења: US National Science Foundation, US Department of Defense
Beyond uniform sampling: Offline reinforcement learning with imbalanced datasets
ZW Hong, A Kumar, S Karnik, A Bhandwaldar, A Srivastava, J Pajarinen, ...
Advances in Neural Information Processing Systems 36, 4985-5009, 2023
Овлашћења: US Department of Defense
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