Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Purposive Learning01:22

Purposive Learning

97
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
97
Reinforcement01:23

Reinforcement

177
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:
177
Behaviorism01:28

Behaviorism

2.2K
The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
2.2K
Reinforcement Schedules01:24

Reinforcement Schedules

129
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
129

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Deep learning-based prediction of lymph node metastasis and occult tumor cells in gastric cancer using histopathological images: a retrospective study.

British journal of cancer·2026
Same author

RUNX1 restrains STAT1-GITRL signaling to shape an immunosuppressive CRC microenvironment.

Cell death discovery·2026
Same author

Single-cell profiling of tumor lineage plasticity and the immune microenvironment in transformed small cell lung cancer.

Journal of translational medicine·2026
Same author

A Type I/II Photosensitizer with Lysosome-Targeting Capabilities Induces Immunogenic Cell Death in Cancer Cells to Enhance Tumor Immunotherapy.

ACS nano·2026
Same author

Hypomethylation-mediated upregulation of PHOX1 promotes gastric cancer progression via transactivation of NGFR.

Cell death discovery·2025
Same author

A Novel Nomogram to Predict Pathological Complete Response in Breast Cancer Patients and Identify Candidates Who Might Omit Surgery: A Large Cohort Study.

Cancer medicine·2025
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·2026
查看所有相关文章

相关实验视频

Updated: May 29, 2025

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

10.9K

基于特征提取的离线提示增强学习方法.

Tianlei Yao1, Xiliang Chen1, Yi Yao1

  • 1College of Command and Control Engineering, Army Engineering University of PLA, Nanjing, China.

PeerJ. Computer science
|February 3, 2025
PubMed
概括
此摘要是机器生成的。

这项研究通过将快速学习与预先训练的模型相结合,增强了线下强化学习. 新方法提高了新环境中的代理人的决策准确性和概括性.

关键词:
大型语言模型.在线非线增强学习.快速学习 快速学习代表性的学习学习.序列建模 序列建模

更多相关视频

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

11.9K
A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

10.5K

相关实验视频

Last Updated: May 29, 2025

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

10.9K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

11.9K
A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

10.5K

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:

  • 线下强化学习 (RL) 从结合变压器和条件策略中获益.
  • 传统的RL代理程序连续处理观测,与收到一系列观测的变压器不同.
  • 变压器在RL中扎于高效的特征提取和分布之外的泛化.

研究的目的:

  • 通过使用少数射击学习和快速学习,增强线下RL的实时政策调整.
  • 提高RL代理人的决策准确性和概括能力.
  • 为了解决变压器在处理顺序RL数据方面的局限性.

主要方法:

  • 利用了预训练模型的几次学习特征.
  • 集成的快速学习,从轨迹样本编码任务信息.
  • 在特征提取轨迹内的细分状态信息块.
  • 将分段序列编码为用于决策生成的GPT模型.

主要成果:

  • 与基线方法相比,拟议的方法显示出更高的性能.
  • 实现了对新环境和任务的增强通用化.
  • 显著提高了代理商决策的稳定性和准确性.

结论:

  • 快速学习和基于序列的特征提取的整合有效地解决了线下RL中的挑战.
  • 该方法为开发更强大,更适应的RL代理提供了一个有希望的方向.
  • 该方法显示了对需要准确和稳定的决策的现实应用的巨大潜力.