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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

107
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...
107
Open and closed-loop control systems01:17

Open and closed-loop control systems

759
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
759
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
PD Controller: Design01:26

PD Controller: Design

241
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
241
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

119
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
119
Feedback control systems01:26

Feedback control systems

316
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
316

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相关实验视频

Updated: Jul 9, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

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一个非政策的多代理随机政策梯度算法,用于合作的连续控制.

Delin Guo1, Lan Tang1, Xinggan Zhang1

  • 1School of Electronic Science and Engineering, Nanjing University, Nanjing, 210093, China.

Neural networks : the official journal of the International Neural Network Society
|December 6, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种政策之外的算法,以提高使用信任区域的多代理强化学习 (MARL) 的数据效率. 这种新方法通过利用历史数据来提高学习绩效,优于现有的方法.

关键词:
深度强化学习 (DRL) 是一种深度强化学习.多个代理的MuJoCoCo可以使用.多代理控制控制多代理控制多个代理强化学习 (MARL)信托地区信托地区

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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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相关实验视频

Last Updated: Jul 9, 2025

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08:18

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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:

  • 信任区域 (TR) 方法在合作的多代理强化学习 (MARL) 中取得了成功.
  • 现有的基于TR的MARL算法主要是基于政策,由于无法使用历史数据,导致样本效率低.
  • 这限制了基于TR的MARL在复杂,数据密集型场景中的实际应用.

研究的目的:

  • 提高基于信任区域的多代理强化学习方法的数据效率.
  • 开发一种政策之外的算法,可以利用历史数据来改进学习.
  • 确保单调的政策改进,同时在信任区域的约束范围内使用历史数据.

主要方法:

  • 为政策之外的优化设计了原始目标函数的近似.
  • 通过优化近似函数与KL分歧约束下的历史数据来证明原始目标的单调改进.
  • 在分散执行的集中培训 (CTDE) 框架内提出了一种实际的非政策的多代理随机政策梯度算法.
  • 综合政策将入奖励中,以鼓励勘探和增强稳定性.

主要成果:

  • 拟议的政策之外的算法在多代理MuJoCo (MAMuJoCo) 基准上明显优于现有的算法.
  • 证明了对历史数据的有效利用,以提高在合作性连续多代理控制任务中的样本效率.
  • 该算法在一系列具有挑战性的合作任务中实现了卓越的性能.

结论:

  • 在政策之外开发的基于TR的MARL算法在数据效率和性能方面提供了实质性的改进.
  • 该方法为利用MARL中的历史数据提供了可行的解决方案,克服了政策方法的局限性.
  • 这项工作推进了合作式多代理连续控制的最新技术.