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A Basal Ganglia Network Centric Autonomous Learning Model and Its Application in Unmanned Aerial Vehicle
Yi, Zeng1,2; Guixiang, Wang1; Bo, Xu1,2; Yi Zeng
2015-12
Conference NameThe 7th International Conference on Brain-inspired Cognitive System
Source PublicationConferences on the 7th International Conference on Brain-inspired Cognitive System
Conference Date2015年12月11-13日
Conference Place安徽合肥
AbstractAutonomous learning paradigms bring flexibility and generality for machine learning, while most of them are mathematical optimization driven approaches, and lack of cognitive evidence. In order to provide a more cognitive driven foundation, in this paper, we develop a basal ganglia network centric autonomous learning model. Compared to existing work on modeling basal ganglia, the work in this paper is unique from the following perspective: (1) Our work takes the orbitofrontal cortex (OFC) into the consideration. The orbitofrontal cortex is critical in decision making because of its responsibility for reward representation and is critical in control of the learning process, while most of the basal ganglia models do not include the orbitofrontal cortex; (2) To compensate the inaccurate memory of numeric values, a method called precise encoding is proposed in this paper to help the working memory remember most of 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; (3) In the information coding process, 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 our basal ganglia network centric autonomous learning model to the Unmanned Aerial Vehicle (UAV) autonomous learning process in a 3D environment. We build the state, action and reward space for the UAV in the environment. Experimental results show that our model is able to give the UAV the ability of free exploration in the environment after an average of 41 trainings.
KeywordAutonomous Learning Model Basal Ganglia Network Precise Encoding Uav Autonomous Learning Reinforcement Learning Interactive Environment.
Subject Area交叉与边缘领域的力学
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12618
Collection类脑智能研究中心
Corresponding AuthorYi Zeng
Affiliation1.Institute of Automation, Chinese Academy of Science
2.CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yi, Zeng,Guixiang, Wang,Bo, Xu,et al. A Basal Ganglia Network Centric Autonomous Learning Model and Its Application in Unmanned Aerial Vehicle[C],2015.
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