Xingyue Liang1, Qiaoyun Wu1, Wenzhang Liu1

  • 1School of Artificial Intelligence, Anhui University, Hefei, 230601, Anhui, China; Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Hefei, 230601, Anhui, China; Anhui Provincial Engineering Research Center for Unmanned Systems and Intelligent Technology, Hefei, 230601, Anhui, China.

概括

本研究引入了一个改进的增强行为者网络 (IP-SAN) 强化学习 (RL). 这种新的方法增强了生物现实性,并在持续控制任务中优于现有的方法.

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