March 13, 2021
Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to the separate dynamics modeling and the subsequent planning algorithm, and as a result, they often possess tens of hyperparameters and architectural choices. For this reason, MBRL typically requires significant human expertise before it can be applied to new problems and domains. To alleviate this problem, we propose to use automatic hyperparameter optimization (HPO). We demonstrate that this problem can be tackled effectively with automated HPO, which we demonstrate to yield significantly improved performance compared to human experts. In addition, we show that tuning of several MBRL hyperparameters dynamically, i.e. during the training itself, further improves the performance compared to using static hyperparameters which are kept fixed for the whole training. Finally, our experiments provide valuable insights into the effects of several hyperparameters, such as plan horizon or learning rate and their influence on the stability of training and resulting rewards.
Written by
Baohe Zhang
Raghu Rajan
Luis Pineda
Nathan Lambert
Andre Biedenkapp
Kurtland Chua
Frank Hutter
Roberto CalandraResearch Topics
Reinforcement Learning
December 07, 2020
To learn good joint policies for multi-agent collaboration with imperfect information remains a fundamental challenge. While for two-player zero-sum games, coordinate-ascent approaches…
Stéphane d’Ascoli, Levent Sagun, Giulio Biroli
December 07, 2020
December 18, 2020
Frequency control is an important problem in modern recommender systems. It dictates the delivery frequency of recommendations to maintain product quality and efficiency.…
Yang Liu, Zhengxing Chen, Kittipat Virochsiri, Juan Wang, Jiahao Wu, Feng Liang
December 18, 2020
December 05, 2020
In the contextual linear bandit setting, algorithms built on the optimism principle fail to exploit the structure of the problem and have been shown to be asymptotically suboptimal. …
Andrea Tirinzonin, Matteo Pirotta, Marcello Restelli, Alessandro Lazaric
December 05, 2020
October 10, 2020
Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing better reconstruction models…
Luis Pineda, Sumana Basu, Adriana Romero,Roberto CalandraRoberto Calandra, Michal Drozdzal
October 10, 2020