首页|Reinforcement Learning Navigation for Robots Based on Hippocampus Episode Cognition

Reinforcement Learning Navigation for Robots Based on Hippocampus Episode Cognition

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Artificial intelligence is currently achieving impressive success in all fields.However,autonomous navigation remains a major challenge for AI.Reinforcement learning is used for target navigation to simulate the interaction between the brain and the environment at the behavioral level,but the Artificial Neural Network trained by reinforcement learning cannot match the autonomous mobility of humans and animals.The hippocampus-striatum circuits are considered as key circuits for target navigation planning and decision-making.This paper aims to construct a bionic navigation model of reinforcement learning corresponding to the nervous system to improve the autonomous navigation performance of the robot.The ventral striatum is considered to be the behavioral evaluation region,and the hippocampal-striatum circuit constitutes the position-reward association.In this paper,a set of episode cognition and reinforcement learning system simulating the mechanism of hip-pocampus and ventral striatum is constructed,which is used to provide target guidance for the robot to perform autonomous tasks.Compared with traditional methods,this system reflects the high efficiency of learning and better Environmental Adaptability.Our research is an exploration of the intersection and fusion of artificial intelligence and neuroscience,which is conducive to the development of artificial intelligence and the understanding of the nervous system.

Episode cognitionReinforcement learningHippocampusRobot navigation

Jinsheng Yuan、Wei Guo、Zhiyuan Hou、Fusheng Zha、Mantian Li、Pengfei Wang、Lining Sun

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State Key Laboratory of Robotics and System,Harbin Institute of Technology(HIT),Harbin 150001,China

School of Mechanical and Electrical Engineering,Lanzhou University of Technology,Lanzhou 730050,China

Shenzhen Academy of Aerospace Technology,Shenzhen 518057,China

National Key R&D Program of ChinaNatural Science Foundation of ChinaNatural Science Foundation of ChinaNatural Science Foundation of ChinaNatural Science Foundation of China

2020YFB13134U2013602520751155152100361911530250

2024

10.1007/s42235-023-00454-7

仿生工程学报(英文版)
吉林大学

仿生工程学报(英文版)

CSTPCDEI
影响因子：0.837
ISSN：1672-6529
年,卷(期)：2024.21(1)
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