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Automated Reinforcement Learning (AutoRL): A Survey and Open Problems. (arXiv:2201.03916v1 [cs.LG])
Jan. 12, 2022, 2:10 a.m. | Jack Parker-Holder, Raghu Rajan, Xingyou Song, André Biedenkapp, Yingjie Miao, Theresa Eimer, Baohe Zhang, Vu Nguyen, Roberto Calandra, Aleksandr
cs.LG updates on arXiv.org arxiv.org
The combination of Reinforcement Learning (RL) with deep learning has led to
a series of impressive feats, with many believing (deep) RL provides a path
towards generally capable agents. However, the success of RL agents is often
highly sensitive to design choices in the training process, which may require
tedious and error-prone manual tuning. This makes it challenging to use RL for
new problems, while also limits its full potential. In many other areas of
machine learning, AutoML has shown …
More from arxiv.org / cs.LG updates on arXiv.org
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