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相关概念视频

Observational Learning01:12

Observational Learning

795
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...
795
Reinforcement Schedules01:24

Reinforcement Schedules

436
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,...
436
Reinforcement01:23

Reinforcement

791
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:
791
Reproductive Cloning01:27

Reproductive Cloning

32.5K
Reproductive cloning is the process of producing a genetically identical copy—a clone—of an entire organism. While clones can be produced by splitting an early embryo—similar to what happens naturally with identical twins—cloning of adult animals is usually done by a process called somatic cell nuclear transfer (SCNT).
Somatic Cell Nuclear Transfer
In SCNT, an egg cell is taken from an animal and its nucleus is removed, creating an enucleated egg. Then a somatic...
32.5K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.3K
Law of Effect01:06

Law of Effect

2.4K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
2.4K

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相关实验视频

Updated: Jan 9, 2026

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits
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结果约束行为克隆用于离线强化学习.

Daolong An1, Chun Shen2, Songlin Li1

  • 1Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun, 130012, China; College of Computer Science and Technology, Jilin University, Changchun, 130012, China.

Neural networks : the official journal of the International Neural Network Society
|November 30, 2025
PubMed
概括

线下强化学习面临着与分布外 (OOD) 状态和行动的挑战. 结果约束行为克隆 (RCBC) 约束结果状态,改善政策学习和实现最先进的绩效.

关键词:
最靠近的邻居搜索搜索在线非线增强学习.没有分布的状态.政策限制 政策限制结果约束结果的限制.

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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:

  • 线下强化学习 (RL) 使用预先收集的数据集进行政策培训.
  • 现有的方法主要针对分布外 (OOD) 动作,忽略了OOD状态.
  • OOD状态和行动显著降低了RL政策的表现.

研究的目的:

  • 引入一种新的方法,结果约束行为克隆 (RCBC),以减轻线下RL中的OD动作和OD状态.
  • 在线数据集的分布中限制学习政策的结果状态.
  • 为了提高线下RL算法的稳定性和准确性.

主要方法:

  • RCBC使用动态模型和最近邻居搜索来强制执行结果状态约束.
  • 结果约束被整合到演员-关键优化过程中.
  • 这鼓励演员选择导致分布状态的动作,并鼓励批评者相信这些动作.

主要成果:

  • RCBC证明了行为克隆的适应性调整.
  • 与现有方法相比,该方法可以实现更准确的价值更新.
  • 分析证实了结果约束机制的有效性.

结论:

  • RCBC有效地解决了OOD行动和离线RL状态的双重问题.
  • 拟议的方法在D4RL基准指标上实现了最先进的性能.
  • RCBC为改善线下RL政策的可靠性提供了一个有希望的方向.