Policy generation network for zero-shot policy learning
文献类型：期刊论文
| 作者 | Qian, Yiming1,2; Zhang, Fengyi1,2 ; Liu, Zhiyong1,2,3
|
| 刊名 | COMPUTATIONAL INTELLIGENCE
 |
| 出版日期 | 2023-07-04 |
| 页码 | 27 |
| ISSN号 | 0824-7935 |
| 关键词 | knowledge representation lifelong reinforcement learning zero-shot policy generation |
| DOI | 10.1111/coin.12591 |
| 通讯作者 | Liu, Zhiyong(zhiyong.liu@ia.ac.cn) |
| 英文摘要 | Lifelong reinforcement learning is able to continually accumulate shared knowledge by estimating the inter-task relationships based on training data for the learned tasks in order to accelerate learning for new tasks by knowledge reuse. The existing methods employ a linear model to represent the inter-task relationships by incorporating task features in order to accomplish a new task without any learning. But these methods may be ineffective for general scenarios, where linear models build inter-task relationships from low-dimensional task features to high-dimensional policy parameters space. Also, the deficiency of calculating errors from objective function may arise in the lifelong reinforcement learning process when some errors of policy parameters restrain others due to inter-parameter correlation. In this paper, we develop a policy generation network that nonlinearly models the inter-task relationships by mapping low-dimensional task features to the high-dimensional policy parameters, in order to represent the shared knowledge more effectively. At the same time, we propose a novel objective function of lifelong reinforcement learning to relieve the deficiency of calculating errors by adding weight constraints for errors. We empirically demonstrate that our method improves the zero-shot policy performance across a variety of dynamical systems. |
| 资助项目 | National Key Research and Development Plan of China[2020AAA0108902] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100] ; National Natural Science Foundation of China[61627808] ; Dongguan Core Technology Research Frontier Project[2019622101001] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | WILEY |
| WOS记录号 | WOS:001022798600001 |
| 资助机构 | National Key Research and Development Plan of China ; Strategic Priority Research Program of Chinese Academy of Science ; National Natural Science Foundation of China ; Dongguan Core Technology Research Frontier Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53663]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Liu, Zhiyong |
| 作者单位 | 1.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 3.Chinese Acad Sci, Cloud Comp Ctr, Dongguan, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qian, Yiming,Zhang, Fengyi,Liu, Zhiyong. Policy generation network for zero-shot policy learning[J]. COMPUTATIONAL INTELLIGENCE,2023:27. |
| APA | Qian, Yiming,Zhang, Fengyi,&Liu, Zhiyong.(2023).Policy generation network for zero-shot policy learning.COMPUTATIONAL INTELLIGENCE,27. |
| MLA | Qian, Yiming,et al."Policy generation network for zero-shot policy learning".COMPUTATIONAL INTELLIGENCE (2023):27. |
入库方式： OAI收割
来源：自动化研究所
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