A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle
文献类型：期刊论文
| 作者 | Zeng, Yi1,2 ; Wang, Guixiang1; Xu, Bo1,2
|
| 刊名 | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
 |
| 出版日期 | 2018-06-01 |
| 卷号 | 10期号:2页码:290-303 |
| 关键词 | Basal ganglia (BG) network brain-inspired intelligence precise encoding reinforcement learning model unmanned aerial vehicle (UAV) autonomous learning |
| ISSN号 | 2379-8920 |
| DOI | 10.1109/TCDS.2017.2649564 |
| 通讯作者 | Zeng, Yi(yi.zeng@ia.ac.cn) |
| 英文摘要 | Reinforcement learning brings flexibility and generality for machine learning, while most of them are mathematical optimization driven approaches, and lack of cognitive and neural evidence. In order to provide a more cognitive and neural mechanisms driven foundation and validate its applicability in complex task, we develop a basal ganglia (BG) network centric reinforcement learning model. Compared to existing work on modeling BG, this paper is unique from the following perspectives: 1) the orbitofrontal cortex (OFC) is taken into consideration. OFC is critical in decision making because of its responsibility for reward representation and is critical in controlling the learning process, while most of the BG centric models do not include OFC; 2) to compensate the inaccurate memory of numeric values, precise encoding is proposed to enable working memory system remember important values during the learning process. The method combines vector convolution and the idea of storage by digit bit and is efficient for accurate value storage; and 3) for information coding, the Hodgkin-Huxley model is used to obtain a more biological plausible description of action potential with plenty of ionic activities. To validate the effectiveness of the proposed model, we apply the model to the unmanned aerial vehicle (UAV) autonomous learning process in a 3-D environment. Experimental results show that our model is able to give the UAV the ability of free exploration in the environment and has comparable learning speed as the Q learning algorithm, while the major advances for our model is that it is with solid cognitive and neural basis. |
| WOS关键词 | ORBITOFRONTAL CORTEX ; FUNCTIONAL-ANATOMY ; DECISION-MAKING ; BRAIN ; CIRCUITS |
| 资助项目 | Chinese Academy of Sciences[XDB02060007] ; Beijing Municipal Commission of Science and Technology[Z151100000915070] ; Beijing Municipal Commission of Science and Technology[Z161100000216124] |
| WOS研究方向 | Computer Science ; Robotics ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000435198600015 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Chinese Academy of Sciences ; Beijing Municipal Commission of Science and Technology |
| 源URL | [http://ir.ia.ac.cn/handle/173211/27937]  |
| 专题 | 类脑智能研究中心_类脑认知计算 |
| 通讯作者 | Zeng, Yi |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zeng, Yi,Wang, Guixiang,Xu, Bo. A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2018,10(2):290-303. |
| APA | Zeng, Yi,Wang, Guixiang,&Xu, Bo.(2018).A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,10(2),290-303. |
| MLA | Zeng, Yi,et al."A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 10.2(2018):290-303. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。