1 code implementation • ICLR 2022 • Fabio Ferreira, Thomas Nierhoff, Andreas Saelinger, Frank Hutter
In a one-to-one comparison, learning an SE proxy requires more interactions with the real environment than training agents only on the real environment.
no code implementations • 11 Jan 2022 • Zhengying Liu, Adrien Pavao, Zhen Xu, Sergio Escalera, Fabio Ferreira, Isabelle Guyon, Sirui Hong, Frank Hutter, Rongrong Ji, Julio C. S. Jacques Junior, Ge Li, Marius Lindauer, Zhipeng Luo, Meysam Madadi, Thomas Nierhoff, Kangning Niu, Chunguang Pan, Danny Stoll, Sebastien Treguer, Jin Wang, Peng Wang, Chenglin Wu, Youcheng Xiong, Arbe r Zela, Yang Zhang
Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly.
1 code implementation • 24 Jan 2021 • Fabio Ferreira, Thomas Nierhoff, Frank Hutter
This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning.