Fetching the paper…
Reading the bibliography…
Recent advances in unsupervised representation learning significantly improved the sample efficiency of training Reinforcement Learning policies in simulated environments.
1906
Earlier work this paper cites.
1910
Earlier work this paper cites.
D. Pomerleau, “ ALVINN: an autonomous land vehicle in a neural network,” in Advances in Neural Information Processing Systems 1, [NIPS Conference, Denver, Colorado, USA, 1988] , D. S. Touretzky, Ed. Morgan Kaufmann, 1988, pp. 305–313. [Online]. Available: http://papers.nips.cc/paper/95-alvinn-an-autonomous-land-vehicle-in-a-neural-network
1988
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
A. Y. Ng and S. J. Russell, “Algorithms for inverse reinforcement learning,” in Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000 , P. Langley, Ed. Morgan Kaufmann, 2000, pp. 663–670
2000
Earlier work this paper cites.
J. Nakanishi, J. Morimoto, G. Endo, G. Cheng, S. Schaal, and M. Kawato, “Learning from demonstration and adaptation of biped locomotion,” Robotics Auton. Syst. , vol. 47, no. 2-3, pp. 79–91, 2004. [Online]. Available: https://doi.org/10.1016/j.robot.2004.03.003
2004
Earlier work this paper cites.
P. Abbeel and A. Y. Ng, “Apprenticeship learning via inverse reinforcement learning,” in Machine Learning, Proceedings of the Twenty-first International Conference (ICML 2004), Banff, Alberta, Canada, July 4-8, 2004 , ser. ACM International Conference Proceeding Series, C. E. Brodley, Ed., vol. 69. ACM, 2004. [Online]. Available: https://doi.org/10.1145/1015330.1015430
2004
Earlier work this paper cites.
R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) , vol. 2. IEEE, 2006, pp. 1735–1742
2006
Earlier work this paper cites.
2008
Earlier work this paper cites.
M. Kalakrishnan, J. Buchli, P. Pastor, and S. Schaal, “Learning locomotion over rough terrain using terrain templates,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 11-15, 2009, St. Louis, MO, USA . IEEE, 2009, pp. 167–172. [Online]. Available: https://doi.org/10.1109/IROS.2009.5354701
2009
Earlier work this paper cites.
2010
Earlier work this paper cites.
S. Ross, G. J. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2011, Fort Lauderdale, USA, April 11-13, 2011 , ser. JMLR Proceedings, G. J. Gordon, D. B. Dunson, and M. Dudík, Eds., vol. 15. JMLR.org, 2011, pp. 627–635. [Online]. Available: http://proceedings.mlr.press/v15/ross11a/ross11a.pdf
2011
Earlier work this paper cites.
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling, “The arcade learning environment: An evaluation platform for general agents,” Journal of Artificial Intelligence Research , vol. 47, pp. 253–279, 2013
2013
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, p. 529, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 815–823
2015
Earlier work this paper cites.
X. Wang and A. Gupta, “Unsupervised learning of visual representations using videos,” in ICCV , 2015
2015
Earlier work this paper cites.
A. Giusti, J. Guzzi, D. C. Ciresan, F. He, J. P. Rodriguez, F. Fontana, M. Faessler, C. Forster, J. Schmidhuber, G. D. Caro, D. Scaramuzza, and L. M. Gambardella, “ A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots ,” IEEE Robotics Autom. Lett. , vol. 1, no. 2, pp. 661–667, 2016. [Online]. Available: https://doi.org/10.1109/LRA.2015.2509024
2015
Earlier work this paper cites.
J. Mahler, F. T. Pokorny, B. Hou, M. Roderick, M. Laskey, M. Aubry, K. Kohlhoff, T. Kröger, J. Kuffner, and K. Goldberg, “Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards,” in ICRA , 2016
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. Hessel, J. Modayil, H. van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. Azar, and D. Silver, “Rainbow: Combining improvements in deep reinforcement learning,” 2017
2017
Cited alongside, same era.
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, and W. Zaremba, “Hindsight experience replay,” in NeurIPS , 2017
2017
Cited alongside, same era.
2019
Later among the works it cites.
P. Florence, L. Manuelli, and R. Tedrake, “Self-supervised correspondence in visuomotor policy learning,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 492–499, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
C. Finn and S. Levine, “Deep visual foresight for planning robot motion,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 2786–2793
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM , vol. 60, no. 6, pp. 84–90, 2017. [Online]. Available: http://doi.acm.org/10.1145/3065386
2017
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
P. R. Florence, L. Manuelli, and R. Tedrake, “ Self-Supervised Correspondence in Visuomotor Policy Learning ,” IEEE Robotics Autom. Lett. , vol. 5, no. 2, pp. 492–499, 2020. [Online]. Available: https://doi.org/10.1109/LRA.2019.2956365
2019
Later among the works it cites.
H. Zhu, A. Gupta, A. Rajeswaran, S. Levine, and V. Kumar, “Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 3651–3657
2019
Later among the works it cites.
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019 . Computer Vision Foundation / IEEE, 2019, pp. 113–123
2019
Later among the works it cites.
A. P. Badia, B. Piot, S. Kapturowski, P. Sprechmann, A. Vitvitskyi, D. Guo, and C. Blundell, “Agent57: Outperforming the atari human benchmark,” in International Conference on Machine Learning , 2020
2020
Closest in time.
2020
Closest in time.
D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to control: Learning behaviors by latent imagination,” in International Conference on Learning Representations , 2020
2020
Closest in time.
2020
Closest in time.
M. Laskin, A. Srinivas, and P. Abbeel, “Curl: Contrastive unsupervised representations for reinforcement learning,” in International Conference on Machine Learning , 2020
2020
Closest in time.
A. Stooke, K. Lee, P. Abbeel, and M. Laskin, “Decoupling representation learning from reinforcement learning,” 2020
2020
Closest in time.
2020
Closest in time.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International Conference on Machine Learning , 2020
2020
Closest in time.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020
2020
Closest in time.
“ xArm 7 .” [Online]. Available: https://store.ufactory.cc/products/xarm-7-2020
2020
Closest in time.
2021
Closest in time.
P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson, “Implicit behavioral cloning,” in Conference on Robot Learning . PMLR, 2022, pp. 158–168
2022
Closest in time.