핵심 개념
Reinforcement learning offers efficient solutions for spatial resource allocation problems by optimizing decision-making processes.
초록
Introduction to the challenge of spatial resource allocation across various domains.
Traditional algorithms face computational pressures, leading to the adoption of reinforcement learning.
Recent advancements in reinforcement learning have shown robust capabilities in various domains.
The paper aims to summarize recent theoretical methods and applied research in spatial resource allocation using reinforcement learning.
Different categories of applications are discussed: static demand resource allocation, static resource allocation, and dynamic resource allocation.
Various algorithms and methodologies are explored in each category.
Challenges and future research directions are highlighted.
통계
"The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life."
"In recent years, reinforcement learning (RL) methods have made breakthroughs in games, Go, autonomous driving, robot control, and pedestrian simulation."
인용구
"Reinforcement learning can achieve nearly real-time decision-making since its training to generate effective models can be performed offline."
"Reinforcement learning provides more ability for large-scale data processing and discovering and extracting their low-level features providing efficient results."