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相关概念视频

Reinforcement01:23

Reinforcement

172
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:
172
Observational Learning01:12

Observational Learning

118
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...
118
Reinforcement Schedules01:24

Reinforcement Schedules

126
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,...
126
Associative Learning01:27

Associative Learning

276
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...
276
Purposive Learning01:22

Purposive Learning

96
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...
96
Introduction to Learning01:18

Introduction to Learning

321
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
321

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辅助基于任务的深度增强学习用于量子控制.

Shumin Zhou, Hailan Ma, Sen Kuang

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    强化学习 (RL) 为量子控制提供了一种新的方法. 一种基于任务的辅助深度RL (AT-DRL) 方法提高了量子系统的控制保真性和学习速度.

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    科学领域:

    • 量子物理学 量子物理学 是一种量子物理学.
    • 人工智能的人工智能
    • 控制理论 控制理论

    背景情况:

    • 量子控制问题很复杂,往往需要特定环境的知识.
    • 强化学习 (RL) 为量子控制提供了一个有希望的,无模型的方法.
    • 深度决定性政策梯度是连续控制任务的关键RL算法.

    研究的目的:

    • 调查使用深度RL的量子系统的连续控制政策的有效性.
    • 提出和评估一个基于任务的辅助深度RL (AT-DRL) 框架,用于高保真度量子控制.
    • 加强量子动力学的探索和改善状态准备.

    主要方法:

    • 使用深度决定性政策梯度实施持续控制政策.
    • 开发一个辅助任务来预测量子系统忠实性,与主要RL任务共享参数.
    • 基于量子状态忠实性的指导奖励函数的设计,用于逐步改进.

    主要成果:

    • 拟议的AT-DRL框架成功地以高保真度控制量子系统.
    • 与标准的RL方法相比,AT-DRL显示了更快的学习率.
    • 数字模拟验证了该方法在探索量子力学方面的有效性.

    结论:

    • AT-DRL为高保真度量子控制和状态准备提供了有效的解决方案.
    • 辅助任务有助于提取内在的环境特征,提高剂的性能.
    • 这种方法显示了设计先进量子控制脉冲的巨大潜力.