Stabilized finite element methods based on multiscale enrichment for Allen-Cahn and Cahn-Hilliard equations

Wen, Juan; He, Yunze; Wang, Kun

doi:10.3934/cpaa.2021074

Cited by 1 publication

(1 citation statement)

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“…The step function is the focus of local path planning strategy implementation based on deep reinforcement learning, which involves two key issues:one is how to describe the observable state space, and the other is how to design the reward function [10] . The former reflects the environmental information that the observer needs to pay attention to during the implementation of local path planning, and the latter can guide the local path planning strategy to update in the direction of the goal [11] .…”

Section: Observable State and Reward Function Designmentioning

confidence: 99%

Digital multi-scale visual planning model of spatial-geographical landscape pattern of smart parks

Yuan,

Wu,

Zhang

2023

Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023)

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The current traditional multi-scale visual planning model mainly relies on existing a priori maps to realize visual planning, which leads to poor planning results due to the lack of establishment of reward functions. In this regard, a digital multiscale visual planning model for the geographic landscape pattern of smart parks is proposed. The AC algorithm is used to describe the observed state and update the planning strategy directly by maximizing the average return, and establish the planning reward function. In the experiments, the planning performance of the proposed model is verified. The analysis of the experimental results shows that the visual planning model constructed by the proposed method possesses a lower collision rate and the model has a better planning performance.

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Section: Observable State and Reward Function Designmentioning

confidence: 99%