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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

147
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,...
147
Instinctive Drift01:05

Instinctive Drift

221
Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
221
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

557
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...
557
Associative Learning01:27

Associative Learning

362
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...
362
Randomized Experiments01:13

Randomized Experiments

6.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.9K
Cognitive Learning01:21

Cognitive Learning

243
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
243

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Operant Procedures for Assessing Behavioral Flexibility in Rats
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有效的离线增强学习与放松的保守主义.

Longyang Huang, Botao Dong, Weidong Zhang

    IEEE transactions on pattern analysis and machine intelligence
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    概括
    此摘要是机器生成的。

    本研究引入了一个新的线下强化学习 (RL) 框架,ORL-RC,以解决保守主义问题. ORL-RC学习了一个更接近真正Q的Q函数的Q函数,提高政策性能和优于现有的方法.

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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:

    • 离线强化学习 (RL) 旨在从静态数据集中学习最佳政策,而无需环境交互.
    • 现有的线下RL方法在学习Q函数和策略中面临着保守主义的挑战,可能会降低性能.
    • 对线下RL保守主义的理论理解需要进一步的研究.

    研究的目的:

    • 提出一个简单而高效的线下RL框架与宽松的保守主义 (ORL-RC).
    • 分析线下RL中学习的Q函数和策略的保守性.
    • 理论上为拟议的ORL-RC框架建立融合和界限.

    主要方法:

    • 开发了离线RL与宽松的保守主义 (ORL-RC) 框架.
    • 在线 RL 中分析了 Q 函数和策略的保守性.
    • 确定了学习Q函数的理论收结果和边界,考虑到抽样错误.

    主要成果:

    • 证明了线下RL中的保守主义可能导致政策绩效退化.
    • 拟议的ORL-RC框架学习了一个更接近真实的Q函数的Q函数.
    • 关于D4RL基准的实验结果表明ORL-RC的表现优于最先进的线下RL方法.

    结论:

    • ORL-RC有效地解决了线下RL中的保守主义问题.
    • 该框架提供了改进的Q函数近似和政策绩效.
    • ORL-RC代表了线下强化学习的重大进步.