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Benjamin Eysenbach
Benjamin Eysenbach
CMU, Google
Zweryfikowany adres z google.com - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Diversity is all you need: Learning skills without a reward function
B Eysenbach, A Gupta, J Ibarz, S Levine
International Conference on Learning Representations, 2019
5742019
Search on the replay buffer: Bridging planning and reinforcement learning
B Eysenbach, R Salakhutdinov, S Levine
Advances in Neural Information Processing Systems, 15246-15257, 2019
1442019
Efficient exploration via state marginal matching
L Lee, B Eysenbach, E Parisotto, E Xing, S Levine, R Salakhutdinov
arXiv preprint arXiv:1906.05274, 2019
1172019
Self-consistent trajectory autoencoder: Hierarchical reinforcement learning with trajectory embeddings
JD Co-Reyes, YX Liu, A Gupta, B Eysenbach, P Abbeel, S Levine
International Conference on Machine Learning, 2018
1082018
Leave No Trace: Learning to reset for safe and autonomous reinforcement learning
B Eysenbach, S Gu, J Ibarz, S Levine
International Conference on Learning Representations, 2018
1032018
Clustervision: Visual supervision of unsupervised clustering
BC Kwon, B Eysenbach, J Verma, K Ng, C De Filippi, WF Stewart, A Perer
IEEE transactions on visualization and computer graphics 24 (1), 142-151, 2017
1032017
Unsupervised meta-learning for reinforcement learning
A Gupta, B Eysenbach, C Finn, S Levine
arXiv preprint arXiv:1806.04640, 2018
982018
Unsupervised curricula for visual meta-reinforcement learning
A Jabri, K Hsu, A Gupta, B Eysenbach, S Levine, C Finn
Advances in Neural Information Processing Systems, 2019
512019
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
B Eysenbach, X Geng, S Levine, R Salakhutdinov
Advances in Neural Information Processing Systems 33, 2020
502020
Learning to Reach Goals via Iterated Supervised Learning
D Ghosh, A Gupta, A Reddy, J Fu, C Devin, B Eysenbach, S Levine
International Conference on Learning Representations, 2021
48*2021
Learning to be safe: Deep rl with a safety critic
K Srinivasan, B Eysenbach, S Ha, J Tan, C Finn
arXiv preprint arXiv:2010.14603, 2020
382020
If MaxEnt RL is the Answer, What is the Question?
B Eysenbach, S Levine
arXiv preprint arXiv:1910.01913, 2019
362019
Maximum Entropy RL (Provably) Solves Some Robust RL Problems
B Eysenbach, S Levine
International Conference on Learning Representations, 2022
292022
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Y Chebotar, K Hausman, Y Lu, T Xiao, D Kalashnikov, J Varley, A Irpan, ...
International Conference on Machine Learning, 2021
272021
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
B Eysenbach, S Chaudhari, S Asawa, S Levine, R Salakhutdinov
International Conference on Learning Representations, 2020
222020
C-Learning: Learning to Achieve Goals via Recursive Classification
B Eysenbach, R Salakhutdinov, S Levine
International Conference on Learning Representations, 2021
202021
Model-Based Visual Planning with Self-Supervised Functional Distances
S Tian, S Nair, F Ebert, S Dasari, B Eysenbach, C Finn, S Levine
International Conference on Learning Representations, 2021
182021
f-IRL: Inverse Reinforcement Learning via State Marginal Matching
T Ni, H Sikchi, Y Wang, T Gupta, L Lee, B Eysenbach
Conference on Robot Learning, 2020
172020
RvS: What is Essential for Offline RL via Supervised Learning?
S Emmons, B Eysenbach, I Kostrikov, S Levine
arXiv preprint arXiv:2112.10751, 2021
132021
Ving: Learning open-world navigation with visual goals
D Shah, B Eysenbach, G Kahn, N Rhinehart, S Levine
2021 IEEE International Conference on Robotics and Automation (ICRA), 13215 …, 2021
132021
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