Towards robust and domain agnostic reinforcement learning competitions

Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Despite this, a majority of challenges suffer from the same fundamental problems: participant solutions to the posed challen...

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Bibliographic Details

Main Authors:	Guss, William Hebgen, Milani, Stephanie, Topin, Nicholay, Houghton, Brandon, Mohanty, Sharada, Melnik, Andrew, Harter, Augustin, Buschmaas, Benoit, Jaster, Bjarne, Berganski, Christoph, Heitkamp, Dennis, Henning, Marko, Ritter, Helge, Wu, Chengjie, Hao, Xiaotian, Lu, Yiming, Mao, Hangyu, Mao, Yihuan, Wang, Chao, Opanowicz, Michal, Kanervisto, Anssi, Schraner, Yanick, Scheller, Christian, Zhou, Xiren, Liu, Lu, Nishio, Daichi, Tsuneda, Toi, Ramanauskas, Karolis, Juceviciute, Gabija
Format:	Journal Article
Language:	English
Published:	07-06-2021
Subjects:	Computer Science - Artificial Intelligence Computer Science - Learning Computer Science - Neural and Evolutionary Computing Computer Science - Robotics Statistics - Machine Learning
Online Access:	Get full text
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