强化标签:用于点特征标签放置的多代理深度强化学习.
IEEE transactions on visualization and computer graphics
|September 11, 2023
概括
本研究介绍了数据可视化中的标签放置的强化学习 (RL),与专家设计的方法相比,实现了更高的标签完整性. 虽然计算密集,但它在优先考虑标签可见性的预计算场景中表现出色.
科学领域:
- 人工智能的人工智能
- 数据可视化 数据可视化
- 机器学习 机器学习
背景情况:
- 深度学习和强化学习 (RL) 在跨领域的复杂问题解决方面表现出色.
- 数据可视化中的标签放置具有挑战性,需要最佳定位以防止重叠并确保可读性.
- 现有的标签放置方法依赖于由人类专家设计的手工制造的算法.
研究的目的:
- 引入一种新的多代理深度强化学习 (MADRL) 方法,用于点特征标签的放置.
- 开发一种基于机器学习的标签放置方法,与传统专家设计的算法形成鲜明对比.
- 评估基于RL的方法与随机和专家设计的策略的性能.
主要方法:
- 使用多剂深度增强学习学习标签放置策略.
- 开发了一个模拟环境,其中代理人作为标签的代理人.
- 经过培训的代理人可以优化标签的位置,以确保完整性和可读性.
主要成果:
- 在标签完整性方面,RL训练的策略明显优于随机策略和专家设计的方法.
- 观察到一个权衡,与现有方法相比,拟议的方法显示计算时间增加.
- 用户研究表明,参与者认为基于RL的方法明显优越.
结论:
- 该MADRL方法为标签放置提供了一个强大的,数据驱动的替代方案,特别是在需要高标签完整性的应用中.
- 该方法非常适合预先计算是可行的场景,例如在地图地图,技术图纸和医疗地图上.
- 提高标签的完整性转化为提高用户感知和主观评估.
相关概念视频
01:29Position Vectors
942
A position vector is a fundamental concept in mathematics that helps determine the position of one point with respect to another point in space. It is a vector that describes the direction and distance between two points. Position vectors are highly useful in the field of math and science, as they help represent spatial relationships and make calculations easier.
For instance, we want to locate a point P(x, y, z) relative to the origin of coordinates O. In that case, we can define a position...
For instance, we want to locate a point P(x, y, z) relative to the origin of coordinates O. In that case, we can define a position...
942
01:23Reinforcement
273
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
273
03:31Labeling DNA Probes
8.2K
DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...
8.2K
01:22Force Classification
1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.3K
01:27Associative Learning
434
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Classical conditioning, also known...
434
01:30Classification of Signals
519
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
519