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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

376
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
376
Reinforcement01:23

Reinforcement

797
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:
797
Reinforcement Schedules01:24

Reinforcement Schedules

438
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,...
438
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.2K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

1.2K
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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相关实验视频

Updated: Jan 11, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

多代理强化学习的动态深度因子图.

Yuchen Shi, Shihong Duan, Cheng Xu

    IEEE transactions on pattern analysis and machine intelligence
    |November 19, 2025
    PubMed
    概括
    此摘要是机器生成的。

    动态深度因素图 (DDFG) 通过动态调整协调策略来增强多代理强化学习 (MARL). 这种新的价值分解方法提高了复杂的多代理系统中的样本效率和稳定性.

    相关实验视频

    Last Updated: Jan 11, 2026

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.6K

    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:

    • 多代理强化学习 (MARL) 在有效协调代理方面面临挑战.
    • 全球价值函数遭受了维度的诅咒,而完全分解的方法可以过度概括.
    • 现有的协调方法与动态的协作模式作斗争.

    研究的目的:

    • 引入动态深度因子图 (DDFG) 以改善MARL中的价值分解.
    • 通过学习动态图形结构来实现适应性协调.
    • 提供对高阶分解的权衡的理论见解.

    主要方法:

    • DDFG使用因子图表表示总值,并动态学习图形结构.
    • 一个图表生成策略适应不断变化的代理关系.
    • 最大总和推理用于有效的联合政策推导.

    主要成果:

    • 在捕食者-猎物和SMAC任务中,DFG在强大的基线上表现出一致的性能增长.
    • 该方法在复杂的MARL环境中提高了样本的效率和稳定性.
    • 理论分析为平衡精度和计算成本提供了指导.

    结论:

    • DDFG提供了一个有效的解决方案,用于MARL的动态协调.
    • 该方法适应不断变化的代理关系,优于现有的方法.
    • 对于解决复杂的多代理协调问题,DFG提出了一个有希望的方向.