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How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for practical applications.
Learning to achieve goals
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Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
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Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael Irwin Jordan · 2008
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Maximum entropy inverse reinforcement learning
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Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2012
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Rich Zemel · 2015
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Human-level control through deep reinforcement learning
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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RL 2 : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
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Chelsea Finn, Paul Christiano, Pieter Abbeel, and Sergey Levine · 2016
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Continuous deep q-learning with model-based acceleration
Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, and Sergey Levine · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
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Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Hindsight experience replay
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A distributional perspective on reinforcement learning
Marc G Bellemare, Will Dabney, and Rémi Munos · 2017
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One-shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly C. Stadie, Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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Shixiang Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E Turner, Bernhard Schölkopf, and Sergey Levine · 2017
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Contextual decision processes with low bellman rank are pac-learnable
Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, and Robert E Schapire · 2017
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Asymmetric actor critic for image-based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2017
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Evolution strategies as a scalable alternative to reinforcement learning
Tim Salimans, Jonathan Ho, Xi Chen, Szymon Sidor, and Ilya Sutskever · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Distributed distributional deterministic policy gradients
Gabriel Barth-Maron, Matthew W Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva Tb, Alistair Muldal, Nicolas Heess, and Timothy Lillicrap · 2018
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Sample-efficient reinforcement learning with stochastic ensemble value expansion
Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, and Honglak Lee · 2018
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Dopamine: A research framework for deep reinforcement learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, and Marc G Bellemare · 2018
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Distributional reinforcement learning with quantile regression
Will Dabney, Mark Rowland, Marc G Bellemare, and Rémi Munos · 2018
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Learning robust rewards with adverserial inverse reinforcement learning
Justin Fu, Katie Luo, and Sergey Levine · 2018
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Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
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Multi-task maximum entropy inverse reinforcement learning
Adam Gleave and Oliver Habryka · 2018
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Unsupervised meta-learning for reinforcement learning
Abhishek Gupta, Benjamin Eysenbach, Chelsea Finn, and Sergey Levine · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Data-Efficient Hierarchical Reinforcement Learning
Ofir Nachum, Shixiang Gu, Honglak Lee, and Sergey Levine · 2018
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Visual reinforcement learning with imagined goals
Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine · 2018
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Human-centric dialog training via offline reinforcement learning
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu, and Rosalind Picard · 2020
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The Hidden Geometry of Particle Collisions
Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler · 2020
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Imitation learning via off-policy distribution matching
Ilya Kostrikov, Ofir Nachum, and Jonathan Tompson · 2020
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Efficient exploration via state marginal matching
Lisa Lee, Benjamin Eysenbach, Emilio Parisotto, Eric Xing, Sergey Levine, and Ruslan Salakhutdinov · 2020
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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Improving language understanding by generative pre-training, 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Rui Zhao and Volker Tresp · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
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