Balancing Depth for Robustness: A Study on Reincarnating Reinforcement Learning Models
Balancing Depth for Robustness: A Study on Reincarnating Reinforcement Learning Models
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Author / Creator
Li, Gang , Wang, Zhuxiao , He, Shaowei , Chen, Xiyuan , Xie, Yunlei , Hu, Jiajun , Wu, Kehe and Jia, Jingping
Publisher
Basel: MDPI AG
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Language
English
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Publisher
Basel: MDPI AG
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Contents
This paper investigates the impact of adaptive network depth selection on the robustness and performance of Regenerative Reinforcement Learning (RRL) models. RRL accelerates learning by reusing previously computed results. We propose a task-driven approach to dynamically configure network depth to explore the best architecture on Atari 2600 games....
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Full title
Balancing Depth for Robustness: A Study on Reincarnating Reinforcement Learning Models
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TN_cdi_doaj_primary_oai_doaj_org_article_da7e43c2c9e44cc0b95751a711631f96
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_da7e43c2c9e44cc0b95751a711631f96
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ISSN
2076-3417
E-ISSN
2076-3417
DOI
10.3390/app15073830
