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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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...
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Reinforcement Schedules01:24

Reinforcement Schedules

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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,...
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相关实验视频

Updated: Jul 8, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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基于多机器人形成的双目标框架的分布式深度强化学习.

Jinming Li1, Qingshan Liu2, Guoyi Chi3

  • 1School of Mathematics, Southeast University, Nanjing 210096, China.

Neural networks : the official journal of the International Neural Network Society
|December 13, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的分布式深度强化学习方法,用于多机器人形成控制,增强概括性和稳定性. 改进的算法实现了更高的奖励和更好的形成维护,证明了更好的灵活性和普遍性.

关键词:
这是一个双目标问题.深度学习是一种深度学习.分布式强化学习的学习.多个机器人组成.神经网络的神经网络的神经网络

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 控制系统 控制系统

背景情况:

  • 多机器人组成控制对于协调任务至关重要.
  • 在多机器人系统中提高通用化能力可以降低培训和计算成本.
  • 现有的方法可能会在动态环境和对新目标位置的概括方面遇到困难.

研究的目的:

  • 开发一种通用的多机器人阵列控制方法.
  • 提高神经网络在多机器人形成任务中的泛化能力.
  • 提出一种分布式的深度强化学习方法,用于强大的形成控制.

主要方法:

  • 利用基于软演员-批评算法的分布式深度强化学习方法.
  • 采用了使用不同神经网络实现不同目标的策略,以提高学习重点.
  • 设计了一个针对分布式培训量身定制的培训评估分配功能.

主要成果:

  • 与原始算法相比,改进的算法实现了更高的累积奖励值.
  • 实验结果表明,在运动过程中,所需的构成在运动过程中得到了更好的维护.
  • 拟议的算法表现出增强的稳定性,由控制信号曲线证明.
  • 由于奖励函数中的旋转设计,该系统在阵营控制中显示出更好的灵活性.

结论:

  • 提出的分布式深度强化学习方法有效地改善了多机器人形成的泛化.
  • 该算法在训练维护和适应训练变化的能力方面表现出强大的性能.
  • 该方法为动态环境中的多机器人系统提供了更高的稳定性和灵活性.