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A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations
Zhao, Feifei1,3; Zeng, Yi1,2,3; Wang, Guixiang1; Bai, Jun1; Xu, Bo1,2,3
Source PublicationCOGNITIVE COMPUTATION
2018-04-01
Volume10Issue:2Pages:296-306
SubtypeArticle
AbstractDecision making is a fundamental ability for intelligent agents (e.g., humanoid robots and unmanned aerial vehicles). During decision making process, agents can improve the strategy for interacting with the dynamic environment through reinforcement learning. Many state-of-the-art reinforcement learning models deal with relatively smaller number of state-action pairs, and the states are preferably discrete, such as Q-learning and Actor-Critic algorithms. While in practice, in many scenario, the states are continuous and hard to be properly discretized. Better autonomous decision making methods need to be proposed to handle these problems. Inspired by the mechanism of decision making in human brain, we propose a general computational model, named as prefrontal cortex-basal ganglia (PFC-BG) algorithm. The proposed model is inspired by the biological reinforcement learning pathway and mechanisms from the following perspectives: (1) Dopamine signals continuously update reward-relevant information for both basal ganglia and working memory in prefrontal cortex. (2) We maintain the contextual reward information in working memory. This has a top-down biasing effect on reinforcement learning in basal ganglia. The proposed model separates the continuous states into smaller distinguishable states, and introduces continuous reward function for each state to obtain reward information at different time. To verify the performance of our model, we apply it to many UAV decision making experiments, such as avoiding obstacles and flying through window and door, and the experiments support the effectiveness of the model. Compared with traditional Q-learning and Actor-Critic algorithms, the proposed model is more biologically inspired, and more accurate and faster to make decision.
KeywordPrefrontal Cortex Working Memory Basal Ganglia Dopamine System Brain-inspired Decision Making Model
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.1007/s12559-017-9511-3
WOS KeywordCOMPUTATIONAL MODEL ; CONNECTIONS ; CIRCUITS ; ORGANIZATION ; STRIATUM
Indexed BySCI
Language英语
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences(XDB02060007) ; Beijing Municipal Commission of Science and Technology(Z161100000216124)
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000430190600010
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22014
Collection类脑智能研究中心
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Feifei,Zeng, Yi,Wang, Guixiang,et al. A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations[J]. COGNITIVE COMPUTATION,2018,10(2):296-306.
APA Zhao, Feifei,Zeng, Yi,Wang, Guixiang,Bai, Jun,&Xu, Bo.(2018).A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations.COGNITIVE COMPUTATION,10(2),296-306.
MLA Zhao, Feifei,et al."A Brain-Inspired Decision Making Model Based on Top-Down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations".COGNITIVE COMPUTATION 10.2(2018):296-306.
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