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相关概念视频

Reinforcement Schedules01:24

Reinforcement Schedules

447
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,...
447
Reinforcement01:23

Reinforcement

816
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:
816
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.1K
Observational Learning01:12

Observational Learning

817
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...
817
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

773
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
773
Transformers in Distribution System01:27

Transformers in Distribution System

491
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
491

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相关实验视频

Updated: Jan 14, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

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多代理深度增强学习与移动充电车辆调度的进化战略

Hua Li, Bongju Jeong

    IEEE transactions on neural networks and learning systems
    |January 12, 2026
    PubMed
    概括
    此摘要是机器生成的。

    移动充电车 (MCV) 为电动汽车 (EV) 充电提供了灵活的解决方案. 一个新的多代理深度强化学习与进化战略 (MARL-ES) 框架优化了MCV调度,以提高效率和利能力.

    相关实验视频

    Last Updated: Jan 14, 2026

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
    11:53

    The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

    Published on: October 14, 2017

    12.1K

    科学领域:

    • 智能运输系统 智能运输系统
    • 运营研究 运营研究
    • 人工智能的人工智能是人工智能.

    背景情况:

    • 固定充电基础设施限制了电动汽车 (EV) 服务.
    • 移动充电车 (MCV) 的动态调度是一个有前途的解决方案.
    • 随机需求和高运营成本挑战了当前的MCV调度.

    研究的目的:

    • 制定一个适应性和高效的MCV调度策略.
    • 为了解决静态和传统MCV调度方法的局限性.
    • 在实时电动汽车充电服务中改善供需平衡.

    主要方法:

    • 制定了MCV调度问题作为马尔科夫决策过程 (MDP).
    • 提出了一个新的多代理深度强化学习 (MARL) 框架与进化战略 (ES) (MARL-ES).
    • 利用分散执行的集中培训 (CTDE) 和集成的行动空间ES,包括突变和基于细分的交叉运营商.

    主要成果:

    • 马尔-ES显著优于静态优化和传统的马尔方法.
    • 在总利方面取得了明显的改善,降低了运营成本,并最大限度地减少了MCV的移动距离.
    • 在不同的车队大小和不确定的条件下展示了强大的可扩展性和适应性.

    结论:

    • 马尔-ES为智能移动充电服务 (MCS) 提供了一种实用且适应性的调度解决方案.
    • 该框架有效地处理电动汽车需求和MCV状态的时空变化.
    • 这种方法提高了电动汽车充电服务的效率和经济可行性.