Averaged-dqn: Variance reduction and stabilization for deep reinforcement learning O Anschel, N Baram, N Shimkin International conference on machine learning, 176-185, 2017 | 357 | 2017 |
Graying the black box: Understanding dqns T Zahavy, N Ben-Zrihem, S Mannor International conference on machine learning, 1899-1908, 2016 | 321 | 2016 |
End-to-end differentiable adversarial imitation learning N Baram, O Anschel, I Caspi, S Mannor International Conference on Machine Learning, 390-399, 2017 | 112 | 2017 |
Long-term planning by short-term prediction S Shalev-Shwartz, N Ben-Zrihem, A Cohen, A Shashua arXiv preprint arXiv:1602.01580, 2016 | 72 | 2016 |
Model-based adversarial imitation learning N Baram, O Anschel, S Mannor arXiv preprint arXiv:1612.02179, 2016 | 45 | 2016 |
Approximate nearest neighbor fields in video N Ben-Zrihem, L Zelnik-Manor Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 22 | 2015 |
Deep reinforcement learning with averaged target DQN O Anschel, N Baram, N Shimkin CoRR abs/1611.01929, 2016 | 21 | 2016 |
Gelato: Geometrically enriched latent model for offline reinforcement learning G Tennenholtz, N Baram, S Mannor arXiv preprint arXiv:2102.11327, 2021 | 5 | 2021 |
Action redundancy in reinforcement learning N Baram, G Tennenholtz, S Mannor Uncertainty in Artificial Intelligence, 376-385, 2021 | 4 | 2021 |
Spatio-temporal abstractions in reinforcement learning through neural encoding N Baram, T Zahavy, S Mannor | 4 | 2016 |
Deep reinforcement learning discovers internal models N Baram, T Zahavy, S Mannor arXiv preprint arXiv:1606.05174, 2016 | 3 | 2016 |
Latent geodesics of model dynamics for offline reinforcement learning G Tennenholtz, N Baram, S Mannor Deep RL Workshop NeurIPS 2021, 2021 | 2 | 2021 |
Maximum entropy reinforcement learning with mixture policies N Baram, G Tennenholtz, S Mannor arXiv preprint arXiv:2103.10176, 2021 | 2 | 2021 |
Inspiration Learning through Preferences N Baram, S Mannor arXiv preprint arXiv:1809.05872, 2018 | 1 | 2018 |
Partial Simulation for Imitation Learning N Baram, S Mannor | | 2019 |