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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

213
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,...
213
Reinforcement01:23

Reinforcement

294
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:
294
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.8K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.8K
Observational Learning01:12

Observational Learning

231
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...
231
Multimachine Stability01:25

Multimachine Stability

198
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
198
Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

126
In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective. 
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant...
126

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相关实验视频

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Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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有效的多任务增强学习,没有性能损失.

Jongchan Baek, Seungmin Baek, Soohee Han

    IEEE transactions on neural networks and learning systems
    |June 7, 2023
    PubMed
    概括

    本研究介绍了一种代稀疏贝叶斯政策优化 (ISBPO) 方法,用于工业控制中的高效多任务强化学习 (RL). ISBPO保留了先前的知识,提高了资源的使用,并提高了持续学习任务的样本效率.

    科学领域:

    • 人工智能的人工智能
    • 机器学习 机器学习
    • 控制系统工程 控制系统工程

    背景情况:

    • 工业控制系统需要高性能和经济高效的解决方案.
    • 强化学习 (RL) 中的持续学习在保持过去的知识,同时获得新的技能方面提出了挑战.

    研究的目的:

    • 为工业控制开发一种高效的多任务强化学习 (RL) 方法.
    • 为持续学习场景提出一个代的稀疏贝叶斯政策优化 (ISBPO) 方案.

    主要方法:

    • 引入了一种代稀疏贝叶斯政策优化 (ISBPO) 方案.
    • 采用代修剪方法来保持以前学习的任务的性能.
    • 使用稀疏贝叶斯政策优化 (SBPO) 来进行修剪意识的政策优化.

    主要成果:

    • 在一个单一的政策神经网络中,ISBPO可实现多个任务的顺序学习.
    • 该方法完全保留了先前学习的任务的控制性能.
    • 通过重量共享和重用,ISBPO提高了学习新任务的样本效率和性能.

    结论:

    • 拟议的ISBPO方案非常适合顺序学习工业控制中的多个任务.

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  • ISBPO在性能保护,高效资源利用和提高样本效率方面表现出有效性.
  • 该方法为复杂的控制应用中持续学习提供了强大的解决方案.