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相关概念视频

Decision Making: P-value Method01:09

Decision Making: P-value Method

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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Decision Making01:20

Decision Making

242
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
242
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.2K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
152
Reinforcement01:23

Reinforcement

353
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:
353
Observational Learning01:12

Observational Learning

319
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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相关实验视频

Updated: Sep 17, 2025

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
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沙普利以价值为导向的多模式深度强化学习,用于复杂的决策.

Jie Zhang1, Boqiang Bao2, Chao Wang3

  • 1Nanjing University, China; Nanjing Research Institute of Electronic Engineering, China.

Neural networks : the official journal of the International Neural Network Society
|June 28, 2025
PubMed
概括

本研究介绍了多模式深度强化学习 (MMDRL),以改善复杂环境中的决策. MMDRL 增强了信息提取,并使用样本增强,以在现实应用中更好地概括和提高效率.

关键词:
合作代理人合作代理人合作复杂的决策过程深度强化学习的学习.多模式学习是多模式学习.沙普利的价值是什么意思

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An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 机器人技术 机器人技术 机器人技术

背景情况:

  • 深度强化学习 (DRL) 在顺序决策方面表现出色,但在复杂的,多式联络的现实环境中扎.
  • 传统的DRL面临的局限性是由于单模数据,样本不足和表示冲突,阻碍了自动驾驶等应用.

研究的目的:

  • 引入一个新的多模式深度强化学习 (MMDRL) 框架,将DRL与多模式学习整合在一起.
  • 在复杂的环境中增强信息提取,利用和决策.
  • 解决多式联运数据整合,样本稀缺和政策优化方面的挑战.

主要方法:

  • 开发了一个MMDRL框架,将深度强化学习与多式模式学习结合起来.
  • 实施了基于知识的样本增强技术,以丰富训练数据并提高概括性.
  • 将环境感知概念化为一个多个代理问题的概念,使用Shapley值来评估模式贡献和优化政策.

主要成果:

  • 拟议的MMDRL框架有效地整合了多式联运信息,以加强决策.
  • 基于知识的样本增量显著改善了模型概括能力.
  • 沙普利基于价值的优化减少了计算复杂性,并在连续行动空间中改进了政策决策.

结论:

  • 在现实世界多式联运场景中,MMDRL为复杂的决策任务提供了强大的解决方案.
  • 该框架在代理决策中表现出卓越的准确性和效率,在MuJoCo和Atari基准上得到了验证.
  • 这项研究推动了DLR应用,为自主系统和其他复杂领域提供了实用方法.