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An Empirical Study of Implicit Regularization in Deep Offline RL


Jul 07, 2022
Caglar Gulcehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matt Hoffman, Razvan Pascanu, Arnaud Doucet

* 40 pages, 37 figures, 2 tables 

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Active Offline Policy Selection


Jun 18, 2021
Ksenia Konyushkova, Yutian Chen, Thomas Paine, Caglar Gulcehre, Cosmin Paduraru, Daniel J Mankowitz, Misha Denil, Nando de Freitas


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On Instrumental Variable Regression for Deep Offline Policy Evaluation


May 21, 2021
Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet


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Regularized Behavior Value Estimation


Mar 17, 2021
Caglar Gulcehre, Sergio G贸mez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew Hoffman, Razvan Pascanu, Nando de Freitas


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Offline Learning from Demonstrations and Unlabeled Experience


Nov 27, 2020
Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Caglar Gulcehre, Ziyu Wang, Yusuf Aytar, Misha Denil, Nando de Freitas, Scott Reed

* Accepted to Offline Reinforcement Learning Workshop at Neural Information Processing Systems (2020) 

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Hyperparameter Selection for Offline Reinforcement Learning


Jul 17, 2020
Tom Le Paine, Cosmin Paduraru, Andrea Michi, Caglar Gulcehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, Nando de Freitas


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RL Unplugged: Benchmarks for Offline Reinforcement Learning


Jul 02, 2020
Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

* 21 pages including supplementary material, the github link for the datasets: https://github.com/deepmind/deepmind-research/rl_unplugged 

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Critic Regularized Regression


Jun 26, 2020
Ziyu Wang, Alexander Novikov, Konrad 呕o艂na, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

* 23 pages 

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Acme: A Research Framework for Distributed Reinforcement Learning


Jun 01, 2020
Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alex Novikov, Sergio G贸mez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas


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