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Deep reinforcement learning (RL) algorithms have achieved great success on a wide variety of sequential decision-making tasks.
Algorithms for inverse reinforcement learning
Andrew Y Ng, Stuart J Russell, et al · 2000
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
Earlier work this paper cites.
Variational information maximization for neural coding
Felix Agakov and David Barber · 2004
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
Earlier work this paper cites.
Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
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 · 2015
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 Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
Cited alongside, same era.
Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Cited alongside, same era.
Rl2: Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
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Mix&match-agent curricula for reinforcement learning
Wojciech Marian Czarnecki, Siddhant M Jayakumar, Max Jaderberg, Leonard Hasenclever, Yee Whye Teh, Simon Osindero, Nicolas Heess, and Razvan Pascanu · 2018
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Transfer learning for related reinforcement learning tasks via image-to-image translation
Shani Gamrian and Yoav Goldberg · 2018
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Beyond sharing weights for deep domain adaptation
Artem Rozantsev, Mathieu Salzmann, and Pascal Fua · 2018
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Kickstarting deep reinforcement learning
Simon Schmitt, Jonathan J Hudson, Augustin Zidek, Simon Osindero, Carl Doersch, Wojciech M Czarnecki, Joel Z Leibo, Heinrich Kuttler, Andrew Zisserman, Karen Simonyan, et al · 2018
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Policy optimization by genetic distillation
Tanmay Gangwani and Jian Peng · 2017
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz
Cited in the paper.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel
Cited in the paper.
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Nervenet: Learning structured policy with graph neural networks
Tingwu Wang, Renjie Liao, Jimmy Ba, and Sanja Fidler · 2018
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Variational information distillation for knowledge transfer
Sungsoo Ahn, Shell Xu Hu, Andreas Damianou, Neil D Lawrence, and Zhenwen Dai · 2019
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Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
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Distilling policy distillation
Wojciech Marian Czarnecki, Razvan Pascanu, Simon Osindero, Siddhant M Jayakumar, Grzegorz Swirszcz, and Max Jaderberg · 2019
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Knowledge flow: Improve upon your teachers
Iou-Jen Liu, Jian Peng, and Alexander G Schwing · 2019
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