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相关概念视频

Reinforcement01:23

Reinforcement

169
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:
169
Observational Learning01:12

Observational Learning

111
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...
111
Reinforcement Schedules01:24

Reinforcement Schedules

123
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,...
123
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.7K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.7K
Fixed Action Patterns01:06

Fixed Action Patterns

15.7K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
15.7K
Cognitive Learning01:21

Cognitive Learning

122
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
122

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Updated: May 21, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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覆盖路径规划使用演员批判性深度强化学习学习

Sergio Isahí Garrido-Castañeda1, Juan Irving Vasquez1, Mayra Antonio-Cruz2

  • 1Centro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Instituto Politécnico Nacional (IPN), Mexico City 07700, Mexico.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了用于移动机器人覆盖路径规划的深度强化学习方法. 像A2C和PPO这样的关键演员方法有效地训练机器人以有效地探索和绘制未知的环境.

关键词:
优势 演员 批评者覆盖路径规划 覆盖路径规划深度强化学习的学习.接近政策优化近接政策优化

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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
  • 机器学习 机器学习

背景情况:

  • 移动机器人探索至关重要,但在完全覆盖环境方面面临挑战.
  • 尽管有进展,现有的覆盖路径规划方法仍然是一个开放的问题.

研究的目的:

  • 为移动机器人覆盖路径规划提出一个深度强化学习框架.
  • 训练和评估关键演员算法,以实现高效的环境探索.

主要方法:

  • 利用深度强化学习与优势行为者-关键 (A2C) 和近接政策优化 (PPO) 算法.
  • 定义的环境状态,观测和奖励功能,为机器人探索量身定制.
  • 对于移动机器人而言,训练有素的政策可以在有障碍的环境中导航.

主要成果:

  • 使用A2C和PPO算法生成了优化的策略.
  • 评估证明了关键演员方法在引导机器人探索中的有效性.
  • 拟议的框架可以有效地覆盖未知的地形.

结论:

  • 行为关键的强化学习方法能够为移动机器人覆盖路径规划制定有效的政策.
  • 这种方法为机器人提供了一个可行的解决方案,可以自主探索和绘制新环境的地图.