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Unsupervised reinforcement learning aims to train agents to learn a handful of policies or skills in environments without external reward.
Optimal transport: old and new , volume 338
Cédric Villani · 2009
Earlier work this paper cites.
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Marco Cuturi · 2013
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Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Human-level control through deep reinforcement learning
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Earlier work this paper cites.
Hindsight experience replay
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Mohammed Amin Abdullah, Aldo Pacchiano, and Moez Draief · 2018
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Wasserstein variational inference
Luca Ambrogioni, Umut Güçlü, Yağmur Güçlütürk, Max Hinne, Marcel A. J. van Gerven, and Eric Maris · 2018
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Wasserstein dependency measure for representation learning
Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, and Pierre Sermanet · 2019
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Skew-fit: State-covering self-supervised reinforcement learning
Vitchyr H Pong, Murtaza Dalal, Steven Lin, Ashvin Nair, Shikhar Bahl, and Sergey Levine · 2019
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Orthogonal estimation of wasserstein distances
Mark Rowland, Jiri Hron, Yunhao Tang, Krzysztof Choromanski, Tamas Sarlos, and Adrian Weller · 2019
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Sliced wasserstein generative models
Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, and Luc Van Gool · 2019
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Relative variational intrinsic control
Kate Baumli, David Warde-Farley, Steven Hansen, and Volodymyr Mnih · 2020
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Meta-reinforcement learning of structured exploration strategies
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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Learning an embedding space for transferable robot skills
Karol Hausman, Jost Tobias Springenberg, Ziyu Wang, Nicolas Heess, and Martin Riedmiller · 2018
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Soheil Kolouri, Gustavo K Rohde, and Heiko Hoffmann · 2018
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Policy optimization as Wasserstein gradient flows
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Explore, discover and learn: Unsupervised discovery of state-covering skills
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Learning to score behaviors for guided policy optimization
Aldo Pacchiano, Jack Parker-Holder, Yunhao Tang, Krzysztof Choromanski, Anna Choromanska, and Michael Jordan · 2020
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Sinkhorn autoencoders
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Primal wasserstein imitation learning
Robert Dadashi, Leonard Hussenot, Matthieu Geist, and Olivier Pietquin · 2021
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Variational intrinsic control revisited
Taehwan Kwon · 2021
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