Pieter Abbeel
Pieter Abbeel
UC Berkeley | Covariant.AI
Verified email at - Homepage
Cited by
Cited by
Model-agnostic meta-learning for fast adaptation of deep networks
C Finn, P Abbeel, S Levine
International conference on machine learning, 1126-1135, 2017
Trust region policy optimization
J Schulman, S Levine, P Abbeel, M Jordan, P Moritz
International conference on machine learning, 1889-1897, 2015
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
T Haarnoja, A Zhou, P Abbeel, S Levine
International conference on machine learning, 1861-1870, 2018
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel
Advances in neural information processing systems 29, 2016
Apprenticeship learning via inverse reinforcement learning
P Abbeel, AY Ng
Proceedings of the twenty-first international conference on Machine learning, 1, 2004
End-to-end training of deep visuomotor policies
S Levine, C Finn, T Darrell, P Abbeel
The Journal of Machine Learning Research 17 (1), 1334-1373, 2016
Multi-agent actor-critic for mixed cooperative-competitive environments
R Lowe, YI Wu, A Tamar, J Harb, OAI Pieter Abbeel, I Mordatch
Advances in neural information processing systems 30, 2017
High-dimensional continuous control using generalized advantage estimation
J Schulman, P Moritz, S Levine, M Jordan, P Abbeel
arXiv preprint arXiv:1506.02438, 2015
Domain randomization for transferring deep neural networks from simulation to the real world
J Tobin, R Fong, A Ray, J Schneider, W Zaremba, P Abbeel
2017 IEEE/RSJ international conference on intelligent robots and systems …, 2017
Introduction to statistical relational learning
D Koller, N Friedman, S Džeroski, C Sutton, A McCallum, A Pfeffer, ...
MIT press, 2007
Hindsight experience replay
M Andrychowicz, F Wolski, A Ray, J Schneider, R Fong, P Welinder, ...
Advances in neural information processing systems 30, 2017
Benchmarking deep reinforcement learning for continuous control
Y Duan, X Chen, R Houthooft, J Schulman, P Abbeel
International conference on machine learning, 1329-1338, 2016
Soft actor-critic algorithms and applications
T Haarnoja, A Zhou, K Hartikainen, G Tucker, S Ha, J Tan, V Kumar, ...
arXiv preprint arXiv:1812.05905, 2018
A simple neural attentive meta-learner
N Mishra, M Rohaninejad, X Chen, P Abbeel
arXiv preprint arXiv:1707.03141, 2017
Sim-to-real transfer of robotic control with dynamics randomization
XB Peng, M Andrychowicz, W Zaremba, P Abbeel
2018 IEEE international conference on robotics and automation (ICRA), 3803-3810, 2018
Reinforcement learning with deep energy-based policies
T Haarnoja, H Tang, P Abbeel, S Levine
International conference on machine learning, 1352-1361, 2017
Discriminative probabilistic models for relational data
B Taskar, P Abbeel, D Koller
arXiv preprint arXiv:1301.0604, 2012
A survey of research on cloud robotics and automation
B Kehoe, S Patil, P Abbeel, K Goldberg
IEEE Transactions on automation science and engineering 12 (2), 398-409, 2015
Guided cost learning: Deep inverse optimal control via policy optimization
C Finn, S Levine, P Abbeel
International conference on machine learning, 49-58, 2016
Denoising diffusion probabilistic models
J Ho, A Jain, P Abbeel
Advances in Neural Information Processing Systems 33, 6840-6851, 2020
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