Saurabh Kumar
Saurabh Kumar
Verified email at stanford.edu
Title
Cited by
Cited by
Year
Dopamine: A research framework for deep reinforcement learning
PS Castro, S Moitra, C Gelada, S Kumar, MG Bellemare
arXiv preprint arXiv:1812.06110, 2018
1442018
Gradient surgery for multi-task learning
T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn
arXiv preprint arXiv:2001.06782, 2020
1052020
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
912019
Federated control with hierarchical multi-agent deep reinforcement learning
S Kumar, P Shah, D Hakkani-Tur, L Heck
arXiv preprint arXiv:1712.08266, 2017
292017
Statistics and samples in distributional reinforcement learning
M Rowland, R Dadashi, S Kumar, R Munos, MG Bellemare, W Dabney
International Conference on Machine Learning, 5528-5536, 2019
272019
Learning to compose skills
H Sahni, S Kumar, F Tejani, C Isbell
arXiv preprint arXiv:1711.11289, 2017
262017
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, 2020
72020
State space decomposition and subgoal creation for transfer in deep reinforcement learning
H Sahni, S Kumar, F Tejani, Y Schroecker, C Isbell
arXiv preprint arXiv:1705.08997, 2017
32017
Characterizing the Gap Between Actor-Critic and Policy Gradient
J Wen, S Kumar, R Gummadi, D Schuurmans
arXiv preprint arXiv:2106.06932, 2021
12021
Multi-Task Reinforcement Learning without Interference
T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn
12019
Generalized Policy Updates for Policy Optimization
S Kumar, Z Ahmed, R Dadashi, D Schuurmans, MG Bellemare
NeurIPS 2019 Optimization Foundations for Reinforcement Learning Workshop, 2019
12019
Mint: Matrix-Interleaving for Multi-Task Learning
T Yu, S Kumar, E Mitchell, A Gupta, K Hausman, S Levine, C Finn
2019
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Articles 1–12