Fetching the paper…
Reading the bibliography…
One of the key issues for imitation learning lies in making policy learned from limited samples to generalize well in the whole state-action space.
Similarity Search in High Dimensions via Hashing. In VLDB’99, Proceedings of 25th International Conference on Very Large Data Bases, September 7-10, 1999, Edinburgh, Scotland, UK . 518–529
Aristides Gionis, Piotr Indyk, and Rajeev Motwani. 1999 · 1999
Earlier work this paper cites.
An analysis of model-based Interval Estimation for Markov Decision Processes
Alexander L. Strehl and Michael L. Littman. 2008 · 2008
Earlier work this paper cites.
Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Inverse Reinforcement Learning
Pieter Abbeel and Andrew Y. Ng. 2010 · 2010
Earlier work this paper cites.
MuJoCo: A physics engine for model-based control. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012, Vilamoura, Algarve, Portugal, October 7-12, 2012 . 5026–5033
Emanuel Todorov, Tom Erez, and Yuval Tassa. [n.d.] · 2012
Earlier work this paper cites.
The Arcade Learning Environment: An Evaluation Platform for General Agents
Marc G. Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling. 2013 · 2013
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. 2015 · 2015
Earlier work this paper cites.
Trust Region Policy Optimization. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015 . 1889–1897
John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, and Philipp Moritz. 2015 · 2015
Earlier work this paper cites.
Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z. Leibo, Jack W. Rae, Daan Wierstra, and Demis Hassabis. 2016 · 2016
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016 · 2016
Earlier work this paper cites.
Generative Adversarial Imitation Learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain . 4565–4573
Jonathan Ho and Stefano Ermon. 2016 · 2016
Cited alongside, same era.
Continuous control with deep reinforcement learning. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings , Yoshua Bengio and Yann LeCun (Eds.)
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2016 · 2016
Cited alongside, same era.
Generalization and Equilibrium in Generative Adversarial Nets (GANs). In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 (Proceedings of Machine Learning Research, Vol. 70) , Doina Precup and Yee Whye Teh (Eds.). 224–232
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang. [n.d.] · 2017
Cited alongside, same era.
Reward learning from human preferences and demonstrations in Atari. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada. 8022–8034
Borja Ibarz, Jan Leike, Tobias Pohlen, Geoffrey Irving, Shane Legg, and Dario Amodei. 2018 · 2018
Later among the works it cites.
Asymmetric Deep Supervised Hashing. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 . 3342–3349
Qing-Yuan Jiang and Wu-Jun Li. 2018 · 2018
Later among the works it cites.
Inverse reinforcement learning for video games
Aaron Tucker, Adam Gleave, and Stuart Russell. 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Spectrally-normalized margin bounds for neural networks. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA . 6241–6250
Peter L. Bartlett, Dylan J. Foster, and Matus J. Telgarsky. 2017 · 2017
Cited alongside, same era.
Deep Reinforcement Learning from Human Preferences. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA . 4302–4310
Paul F. Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
Cited alongside, same era.
OpenAI Baselines
Prafulla Dhariwal, Christopher Hesse, Oleg Klimov, Alex Nichol, Matthias Plappert, Alec Radford, John Schulman, Szymon Sidor, Yuhuai Wu, and Peter Zhokhov. 2017 · 2017
Cited alongside, same era.
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Cited alongside, same era.
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA . 2750–2759
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, and Pieter Abbeel. 2017 · 2017
Cited alongside, same era.
Playing hard exploration games by watching YouTube. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada. 2935–2945
Yusuf Aytar, Tobias Pfaff, David Budden, Thomas Paine, Ziyu Wang, and Nando de Freitas. 2018 · 2018
Cited alongside, same era.
Deep Q-learning From Demonstrations. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018 . 3223–3230
Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Dan Horgan, John Quan, Andrew Sendonaris, Ian Osband, Gabriel Dulac-Arnold, John Agapiou, Joel Z. Leibo, and Audrunas Gruslys. 2018 · 2018
Cited alongside, same era.
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations. In Proceedings of the 3rd Conference on Robot Learning
Daniel S. Brown, Wonjoon Goo, and Scott Niekum. 2019a
Cited in the paper.
Haiyan Yin, Jianda Chen, and Sinno Jialin Pan. 2018 · 2018
Later among the works it cites.
On the Discrimination-Generalization Tradeoff in GANs. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings
Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, and Xiaodong He. 2018 · 2018
Later among the works it cites.
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA . 783–792
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, and Scott Niekum. 2019b · 2019
Closest in time.
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, and Sergey Levine. 2019 · 2019
Closest in time.
Virtual-Taobao: Virtualizing Real-World Online Retail Environment for Reinforcement Learning. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019 . 4902–4909
Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, and Anxiang Zeng. 2019 · 2019
Closest in time.
Intrinsic Reward Driven Imitation Learning via Generative Model. In International Conference on Machine Learning . 6672–6682
Xingrui Yu, Yueming Lyu, and Ivor Tsang. 2020 · 2020
Closest in time.
Deep Supervised Hashing for Fast Image Retrieval. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 . 2064–2072
Haomiao Liu, Ruiping Wang, Shiguang Shan, and Xilin Chen. 2016 · 2072
Closest in time.