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Despite significant progress, deep reinforcement learning (RL) suffers from data-inefficiency and limited generalization.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Richard S Sutton, Andrew G Barto, et al · 1998
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Mujoco: A physics engine for model-based control
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller · 2013
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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 · 2015
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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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End-to-end training of deep visuomotor policies
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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
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Continuous adaptation via meta-learning in nonstationary and competitive environments
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, and Pieter Abbeel · 2017
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Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Stochastic neural networks for hierarchical reinforcement learning
Carlos Florensa, Yan Duan, and Pieter Abbeel · 2017
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Meta-sgd: Learning to learn quickly for few shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li · 2017
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Meta-learning with temporal convolutions
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
Meta-reinforcement learning of structured exploration strategies
Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, and Sergey Levine · 2018
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Rein Houthooft, Richard Y Chen, Phillip Isola, Bradly C Stadie, Filip Wolski, Jonathan Ho, and Pieter Abbeel · 2018
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Transferable meta learning across domains
Bingyi Kang and Jiashi Feng · 2018
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Modeling task relationships in multi-task learning with multi-gate mixture-of-experts
Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi · 2018
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Some considerations on learning to explore via meta-reinforcement learning
Bradly C Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, and Ilya Sutskever · 2018
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Ignasi Clavera, Anusha Nagabandi, Ronald S Fearing, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
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Meta learning shared hierarchies
Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, and John Schulman · 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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Transfer of value functions via variational methods
Andrea Tirinzoni, Rafael Rodriguez Sanchez, and Marcello Restelli · 2018
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Learning a prior over intent via meta-inverse reinforcement learning
Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, and Chelsea Finn · 2018
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Learning to explore with meta-policy gradient
Tianbing Xu, Qiang Liu, Liang Zhao, Wei Xu, and Jian Peng · 2018
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Meta-gradient reinforcement learning
Zhongwen Xu, Hado van Hasselt, and David Silver · 2018
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