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The information bottleneck principle is an elegant and useful approach to representation learning.
The information bottleneck method
Naftali Tishby, Fernando C. Pereira, and William Bialek · 2000
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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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Generative adversarial nets
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Xu Bing, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
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Deep reinforcement learning with double q-learning
Hado Van Hasselt, Arthur Guez, and David Silver · 2015
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Deep variational information bottleneck
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy · 2016
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Stein variational gradient descent: A general purpose bayesian inference algorithm
Qiang Liu and Dilin Wang · 2016
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, and Koray Kavukcuoglu · 2016
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Learning from the memory of atari 2600
Jakub Sygnowski and Henryk Michalewski · 2016
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Reinforcement learning with deep energy-based policies
Tuomas Haarnoja, Haoran Tang, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Stein variational policy gradient
Yang Liu, Prajit Ramachandran, Qiang Liu, and Jian Peng · 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.
Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
Cited alongside, same era.
Mine: Mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and R Devon Hjelm · 2018
Learning latent dynamics for planning from pixels
Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, and James Davidson · 2018
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State representation learning for control: An overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean François Goudou, and David Filliat · 2018
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Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shane Gu, Honglak Lee, and Sergey Levine · 2018
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Near-optimal representation learning for hierarchical reinforcement learning
Ofir Nachum, Shixiang Gu, Honglak Lee, and Sergey Levine · 2018
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Variational discriminator bottleneck: Improving imitation learning, inverse rl, and gans by constraining information flow
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, and Sergey Levine · 2018
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State abstraction as compression in apprenticeship learning
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Cited alongside, same era.
Reward constrained policy optimization
Tessler Chen, Daniel J. Mankowitz, and Shie Mannor · 2018
Cited alongside, same era.
Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Volodymir Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, and Iain Dunning · 2018
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Van Hoof, and David Meger · 2018
Cited alongside, same era.
David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L Littman, and Lawson LS Wong · 2019
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A geometric perspective on optimal representations for reinforcement learning
Marc G. Bellemare, Will Dabney, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, and Clare Lyle · 2019
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Reinforcement learning, fast and slow
Mathew Botvinick, Sam Ritter, Jane X Wang, Zeb Kurth-Nelson, Charles Blundell, and Demis Hassabis · 2019
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