Optimization of the Stand Structure in Secondary Forests of Pinus yunnanensis Based on Deep Reinforcement Learning

被引：0
|
作者：
Zhao, Jian [1 ]
Wang, Jianmming [1 ]
Yin, Jiting [2 ]
Chen, Yuling [3 ]
Wu, Baoguo [4 ]
机构：
[1] School of Mathematics and Computer Science, Dali University, Dali,671003, China
[2] Dali Forestry and Grassland Science Research Institute, Dali,671000, China
[3] Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing,100871, China
[4] School of Information Science and Technology, Beijing Forestry University, Beijing,100083, China
来源：
Forests | 2024年 / 15卷 / 12期
关键词：
All Open Access; Gold;
D O I：
10.3390/f15122181
中图分类号：
学科分类号：
摘要：
60
引用
收藏
相关论文
共 50 条
  • [31] Reactive Power Optimization Based on Proximal Policy Optimization of Deep Reinforcement Learning
    Zahng P.
    Zhu Z.
    Xie H.
    Dianwang Jishu/Power System Technology, 2023, 47 (02): : 562 - 570
  • [32] Structure and stand development of secondary forests in Eastern Prealps (Italy)
    Alberti, Giorgio
    Peressotti, Alessandro
    Piussi, Pietro
    Zerbi, Giuseppe
    DENDROCHRONOLOGIA, 2009, 27 (03) : 173 - 181
  • [33] Thinning Effects on Stand Structure and Carbon Content of Secondary Forests
    Wang, Zichun
    Li, Yaoxiang
    Meng, Yongbin
    Li, Chunxu
    Zhang, Zheyu
    FORESTS, 2022, 13 (04):
  • [34] Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems
    Zhang, Zizhen
    Wu, Zhiyuan
    Zhang, Hang
    Wang, Jiahai
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) : 7978 - 7991
  • [35] Protein Secondary Structure Prediction Based on Deep Learning
    Zheng, Lin
    Li, Hong-ling
    Wu, Nan
    Ao, Li
    3RD INTERNATIONAL SYMPOSIUM ON MECHATRONICS AND INDUSTRIAL INFORMATICS, (ISMII 2017), 2017, : 171 - 177
  • [36] Changes in the understory diversity of secondary Pinus tabulaeformis forests are the result of stand density and soil properties
    Zhang, Yunxiang
    Liu, Tairui
    Guo, Jingping
    Tan, Zhijie
    Dong, Wei
    Wang, HuiHui
    GLOBAL ECOLOGY AND CONSERVATION, 2021, 28
  • [37] An Intelligent Optimization Strategy Based on Deep Reinforcement Learning for Step Counting
    Sun, Zhoubao
    Chen, Pengfei
    Zhang, Xiaodong
    DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2021, 2021
  • [38] Trajectory optimization algorithm of skipping missile based on deep reinforcement learning
    Gong K.
    Wei H.
    Li J.
    Song X.
    Li Y.
    Li Y.
    Zhang Y.
    Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics, 2023, 49 (06): : 1383 - 1393
  • [39] Research Reviews of Combinatorial Optimization Methods Based on Deep Reinforcement Learning
    Li, Kai-Wen
    Zhang, Tao
    Wang, Rui
    Qin, Wei-Jian
    He, Hui-Hui
    Huang, Hong
    Zidonghua Xuebao/Acta Automatica Sinica, 2021, 47 (11): : 2521 - 2537
  • [40] Deep Reinforcement Learning Based Optimization and Risk Control of Trading Strategies
    Bao, Mengrui
    JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (05) : 241 - 252
12345