all AI news
On Effective Scheduling of Model-based Reinforcement Learning. (arXiv:2111.08550v3 [cs.LG] UPDATED)
July 6, 2022, 1:11 a.m. | Hang Lai, Jian Shen, Weinan Zhang, Yimin Huang, Xing Zhang, Ruiming Tang, Yong Yu, Zhenguo Li
stat.ML updates on arXiv.org arxiv.org
Model-based reinforcement learning has attracted wide attention due to its
superior sample efficiency. Despite its impressive success so far, it is still
unclear how to appropriately schedule the important hyperparameters to achieve
adequate performance, such as the real data ratio for policy optimization in
Dyna-style model-based algorithms. In this paper, we first theoretically
analyze the role of real data in policy training, which suggests that gradually
increasing the ratio of real data yields better performance. Inspired by the
analysis, we …
arxiv learning lg reinforcement reinforcement learning scheduling
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Senior Data Analyst - SQL
@ Experian | Heredia, Costa Rica
Lead Business Intelligence Developer
@ L.A. Care Health Plan | Los Angeles, CA, US, 90017
(USA) Senior Manager, Data Analytics
@ Walmart | (USA) AR BENTONVILLE Home Office J Street Offices, Suite #2
Autonomous Haulage System Application Specialist
@ Komatsu | Belo Horizonte, BR
Machine Learning Engineer
@ GFT Technologies | Alcobendas, M, ES, 28108