Time-sequence Action-Decision and Navigation Through Stage Deep Reinforcement Learning in Complex Dynamic Environments | |
Huimu, Wang1,3; Tenghai, Qiu1; Zhen, Liu1; Zhiqiang, Pu1,3; Jianqiang, Yi1,3; Zhaoyang, Liu2 | |
2019 | |
会议日期 | 2019.12 |
会议地点 | 厦门 |
英文摘要 | Navigation in a complex dynamic environment is one of the most attractive tasks. Although most of such algorithms can achieve navigation tasks effectively, they ignore the necessity of the mission planning in the process of navigation. Given the situation, a novel end-to-end two-stage deep reinforcement learning architecture for a time-sequence navigation and action-decision in a dynamic environment with randomly rapidly |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44953] |
专题 | 综合信息系统研究中心_飞行器智能技术 |
通讯作者 | Tenghai, Qiu |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.Department of Automation, Tsinghua University 3.School of Artificial Intelligence, University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Huimu, Wang,Tenghai, Qiu,Zhen, Liu,et al. Time-sequence Action-Decision and Navigation Through Stage Deep Reinforcement Learning in Complex Dynamic Environments[C]. 见:. 厦门. 2019.12. |
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