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MBRL-Lib: A Modular Library for Model-based Reinforcement Learning


Apr 20, 2021
Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra


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Off-Belief Learning


Mar 06, 2021
Hengyuan Hu, Adam Lerer, Brandon Cui, Luis Pineda, David Wu, Noam Brown, Jakob Foerster


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On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning


Feb 26, 2021
Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra

* 19 pages, accepted by AISTATS 2021 

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Active MR k-space Sampling with Reinforcement Learning


Jul 20, 2020
Luis Pineda, Sumana Basu, Adriana Romero, Roberto Calandra, Michal Drozdzal

* To appear in 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2020 

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On the Evaluation of Conditional GANs


Jul 11, 2019
Terrance DeVries, Adriana Romero, Luis Pineda, Graham W. Taylor, Michal Drozdzal


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Learning Causal State Representations of Partially Observable Environments


Jun 25, 2019
Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello

* 16 pages, 11 figures 

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Elucidating image-to-set prediction: An analysis of models, losses and datasets


Apr 11, 2019
Luis Pineda, Amaia Salvador, Michal Drozdzal, Adriana Romero


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Planning in Stochastic Environments with Goal Uncertainty


Oct 18, 2018
Sandhya Saisubramanian, Kyle Hollins Wray, Luis Pineda, Shlomo Zilberstein

* 8 pages, 5 figures 

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Generalizing the Role of Determinization in Probabilistic Planning


Jul 29, 2017
Luis Pineda, Shlomo Zilberstein


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