Ofir Nachum
Ofir Nachum
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D4rl: Datasets for deep data-driven reinforcement learning
J Fu, A Kumar, O Nachum, G Tucker, S Levine
arXiv preprint arXiv:2004.07219, 2020
Data-Efficient Hierarchical Reinforcement Learning
O Nachum, S Gu, H Lee, S Levine
Advances in Neural Information Processing Systems, 2018
Behavior regularized offline reinforcement learning
Y Wu, G Tucker, O Nachum
arXiv preprint arXiv:1911.11361, 2019
A Lyapunov-based Approach to Safe Reinforcement Learning
Y Chow, O Nachum, E Duenez-Guzman, M Ghavamzadeh
Advances in Neural Information Processing Systems, 2018
Rt-1: Robotics transformer for real-world control at scale
A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ...
arXiv preprint arXiv:2212.06817, 2022
Bridging the gap between value and policy based reinforcement learning
O Nachum, M Norouzi, K Xu, D Schuurmans
Advances in neural information processing systems 30, 2017
Learning to remember rare events
Ł Kaiser, O Nachum, A Roy, S Bengio
International Conference for Learning Representations, 2017
Morphnet: Fast & simple resource-constrained structure learning of deep networks
A Gordon, E Eban, O Nachum, B Chen, H Wu, TJ Yang, E Choi
Proceedings of the IEEE conference on computer vision and pattern …, 2018
Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections
O Nachum, Y Chow, B Dai, L Li
Advances in neural information processing systems 32, 2019
Identifying and correcting label bias in machine learning
H Jiang, O Nachum
International conference on artificial intelligence and statistics, 702-712, 2020
Deepmdp: Learning continuous latent space models for representation learning
C Gelada, S Kumar, J Buckman, O Nachum, MG Bellemare
International conference on machine learning, 2170-2179, 2019
Offline reinforcement learning with fisher divergence critic regularization
I Kostrikov, R Fergus, J Tompson, O Nachum
International Conference on Machine Learning, 5774-5783, 2021
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
D Quillen, E Jang, O Nachum, C Finn, J Ibarz, S Levine
IEEE International Conference on Robotics and Automation, 2018
Lyapunov-based safe policy optimization for continuous control
Y Chow, O Nachum, A Faust, E Duenez-Guzman, M Ghavamzadeh
arXiv preprint arXiv:1901.10031, 2019
Algaedice: Policy gradient from arbitrary experience
O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans
arXiv preprint arXiv:1912.02074, 2019
Near-optimal representation learning for hierarchical reinforcement learning
O Nachum, S Gu, H Lee, S Levine
arXiv preprint arXiv:1810.01257, 2018
Imitation learning via off-policy distribution matching
I Kostrikov, O Nachum, J Tompson
arXiv preprint arXiv:1912.05032, 2019
Multi-game decision transformers
KH Lee, O Nachum, MS Yang, L Lee, D Freeman, S Guadarrama, ...
Advances in Neural Information Processing Systems 35, 27921-27936, 2022
Rl unplugged: A suite of benchmarks for offline reinforcement learning
C Gulcehre, Z Wang, A Novikov, T Paine, S Gómez, K Zolna, R Agarwal, ...
Advances in Neural Information Processing Systems 33, 7248-7259, 2020
Opal: Offline primitive discovery for accelerating offline reinforcement learning
A Ajay, A Kumar, P Agrawal, S Levine, O Nachum
arXiv preprint arXiv:2010.13611, 2020
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