Knowledge Commons of Institute of Automation,CAS
Time-sequence Action-Decision and Navigation Through Stage Deep Reinforcement Learning in Complex Dynamic Environments | |
Huimu, Wang1,2; Tenghai, Qiu2; Zhen, Liu2; Zhiqiang, Pu1,2; Jianqiang, Yi1,2; Zhaoyang, Liu3 | |
2019 | |
会议名称 | 2019 IEEE Symposium Series on Computational Intelligence |
会议日期 | 2019.12 |
会议地点 | 厦门 |
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 |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 强化与进化学习 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44953 |
专题 | 复杂系统认知与决策实验室_飞行器智能技术 |
通讯作者 | Tenghai, Qiu |
作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences 2.Institute of Automation, Chinese Academy of Sciences 3.Department of Automation, Tsinghua University |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
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],2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Time-sequence Action(2178KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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