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相关概念视频

Probability Distributions01:32

Probability Distributions

6.8K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
6.8K
Reinforcement Schedules01:24

Reinforcement Schedules

138
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,...
138
Uniform Distribution01:19

Uniform Distribution

4.8K
The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
4.8K
State Space Representation01:27

State Space Representation

178
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
178
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

222
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
222
Poisson Probability Distribution01:09

Poisson Probability Distribution

7.8K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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相关实验视频

Updated: Jun 15, 2025

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

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用单模式概率分布分离连续行动空间,用于政策上的强化学习.

Yuanyang Zhu, Zhi Wang, Yuanheng Zhu

    IEEE transactions on neural networks and learning systems
    |August 27, 2024
    PubMed
    概括

    在强化学习 (RL) 中分离连续行动空间可以增加差异. 本研究介绍了一种使用Poisson分布的单模政策,以提高复杂控制任务中的稳定性和性能.

    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:

    • 在政策强化学习 (RL) 中分离连续行动空间简化了优化,但由于忽视了行动顺序,可能导致高差异.
    • 在不考虑其固有的顺序的情况下,离散行动的爆炸可能会对政策梯度 (PG) 估计器的性能产生负面影响.

    研究的目的:

    • 引入对政策上RL的新架构,限制离散政策成为单模式.
    • 通过明确的单模式概率分布,利用潜在的连续动作空间的连续性.
    • 减少政策梯度估计器的差异,提高学习稳定性.

    主要方法:

    • 使用波桑概率分布实现单模分立政策架构.
    • 将政策限制为单模式,以更好地利用行动空间的连续性.
    • 在具有挑战性的控制任务上进行广泛的实验,包括人形任务.

    主要成果:

    • 与标准方法相比,单模式离散政策实现了明显更快的趋同.
    • 在复杂的政策性RL任务中表现更高,特别是在具有高度挑战性的场景中,如Humanoid.
    • 理论分析证实,在拟议的单模式政策下,PG估计器的差异较小.

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    结论:

    • 拟议的单模式离散政策架构有效地解决了与在政策RL中的行动空间离散相关的差异问题.
    • 这种方法提高了学习稳定性和性能,特别是在复杂的机器人控制任务中.
    • 单模式概率分布的明确使用更好地利用了行动空间的连续性,以改善RL结果.