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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

243
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,...
243
Reinforcement01:23

Reinforcement

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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
Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

148
To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
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Observational Learning01:12

Observational Learning

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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...
319
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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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...
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Purposive Learning01:22

Purposive Learning

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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...
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脚手架跳跃与生成强化学习学习

Luke Rossen1,2, Finton Sirockin2, Nadine Schneider2

  • 1Institute for Complex Molecular Systems (ICMS), Department of Biomedical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.

Journal of chemical information and modeling
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了强化学习用于不受约束的脚手架跳跃 (RuSH),这是一种用于药物发现的新型计算方法. RuSH可实现全分子生成,增强架架跳跃多样性,同时保持关键的分子性质.

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科学领域:

  • 药用化学 医学化学
  • 计算化学计算化学
  • 药物发现 药物发现 药物发现

背景情况:

  • 脚手架跳跃对于药物发现至关重要,但对化学家和计算方法具有挑战性.
  • 现有的方法通常将脚手架跳跃限制在预定义的基层结构上,限制化学太空探索.
  • 生成强化学习提供了通过物业优化加速脚手架跳跃的潜力.

研究的目的:

  • 通过实现不受约束的全分子生成,推进支架跳跃的强化学习.
  • 开发一种方法,产生具有高3D和药相似性的新型支架,但与参考分子的支架相似性较低.
  • 证明拟议方法在探索类似物和设计新的跳候选者的有效性.

主要方法:

  • 引入了用于不受约束的脚手架跳跃 (RuSH) 的强化学习方法.
  • RuSH利用生成强化学习来实现全分子生成.
  • 该方法优化了高3D和药相似性,同时最大限度地降低了支架相似性.

主要成果:

  • 在探索已知的脚手架的类型方面,RuSH表现出灵活性和有效性.
  • 这种方法成功地设计了与已知的结合机制相匹配的跳候选者.
  • 与既有方法进行比较,突出了RuSH实现脚手架多样性和保存3D属性的能力.

结论:

  • 通过实现不受约束的,全分子生成,RuSH推进了脚手架跳跃.
  • 该方法系统地实现了支架的多样性,同时保持了关键的三维属性.
  • 通过新架设计,RuSH提供了一种强大的工具,可以加速药物发现.