Hierarchical Reinforcement Learning With Automatic Sub-Goal Identification
文献类型:期刊论文
作者 | Chenghao Liu; Fei Zhu![]() |
刊名 | IEEE/CAA Journal of Automatica Sinica
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出版日期 | 2021 |
卷号 | 8期号:10页码:1686-1696 |
关键词 | Hierarchical control hierarchical reinforcement learning option sparse reward sub-goal |
ISSN号 | 2329-9266 |
DOI | 10.1109/JAS.2021.1004141 |
英文摘要 | In reinforcement learning an agent may explore ineffectively when dealing with sparse reward tasks where finding a reward point is difficult. To solve the problem, we propose an algorithm called hierarchical deep reinforcement learning with automatic sub-goal identification via computer vision (HADS) which takes advantage of hierarchical reinforcement learning to alleviate the sparse reward problem and improve efficiency of exploration by utilizing a sub-goal mechanism. HADS uses a computer vision method to identify sub-goals automatically for hierarchical deep reinforcement learning. Due to the fact that not all sub-goal points are reachable, a mechanism is proposed to remove unreachable sub-goal points so as to further improve the performance of the algorithm. HADS involves contour recognition to identify sub-goals from the state image where some salient states in the state image may be recognized as sub-goals, while those that are not will be removed based on prior knowledge. Our experiments verified the effect of the algorithm. |
源URL | [http://ir.ia.ac.cn/handle/173211/45382] ![]() |
专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
推荐引用方式 GB/T 7714 | Chenghao Liu,Fei Zhu,Quan Liu,et al. Hierarchical Reinforcement Learning With Automatic Sub-Goal Identification[J]. IEEE/CAA Journal of Automatica Sinica,2021,8(10):1686-1696. |
APA | Chenghao Liu,Fei Zhu,Quan Liu,&Yuchen Fu.(2021).Hierarchical Reinforcement Learning With Automatic Sub-Goal Identification.IEEE/CAA Journal of Automatica Sinica,8(10),1686-1696. |
MLA | Chenghao Liu,et al."Hierarchical Reinforcement Learning With Automatic Sub-Goal Identification".IEEE/CAA Journal of Automatica Sinica 8.10(2021):1686-1696. |
入库方式: OAI收割
来源:自动化研究所
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