文献类型：期刊文献

英文题名：AnimalEnvNet: A Deep Reinforcement Learning Method for Constructing Animal Agents Using Multimodal Data Fusion

作者：Chen, Zhao[1,2] Wang, Dianchang[1,2] Zhao, Feixiang[1,2] Dai, Lingnan[1,2] Zhao, Xinrong[1,2] Jiang, Xian[3] Zhang, Huaiqing[3]

第一作者：Chen, Zhao

通信作者：Zhang, HQ[1]

机构：[1]Beijing Forestry Univ, Sch Informat Sci & Technol, Beijing 100083, Peoples R China;[2]Natl Forestry & Grassland Adm, Engn Res Ctr Forestry Oriented Intelligent Informa, Beijing 100083, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2024

卷号：14

期号：14

外文期刊名：APPLIED SCIENCES-BASEL

收录：;EI(收录号：20243116778637);Scopus(收录号：2-s2.0-85199626801);WOS:【SCI-EXPANDED(收录号:WOS:001276621600001)】；

语种：英文

外文关键词：deep reinforcement learning; AnimalEnvNet; multimodal data fusion; animal movement behaviour mode; CNN; LSTM

摘要：Simulating animal movement has long been a central focus of study in the area of wildlife behaviour studies. Conventional modelling methods have difficulties in accurately representing changes over time and space in the data, and they generally do not effectively use telemetry data. Thus, this paper introduces a new and innovative deep reinforcement learning technique known as AnimalEnvNet. This approach combines historical trajectory data and remote sensing images to create an animal agent using deep reinforcement learning techniques. It overcomes the constraints of conventional modelling approaches. We selected pandas as the subject of our research and carried out research using GPS trajectory data, Google Earth images, and Sentinel-2A remote sensing images. The experimental findings indicate that AnimalEnvNet reaches convergence during supervised learning training, attaining a minimal mean absolute error (MAE) of 28.4 m in single-step prediction when compared to actual trajectories. During reinforcement learning training, the agent has the capability to replicate animal locomotion for a maximum of 12 iterations, while maintaining an error margin of 1000 m. This offers a novel approach and viewpoint for mimicking animal behaviour.