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相关概念视频

Observational Learning01:12

Observational Learning

222
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...
222
Reinforcement Schedules01:24

Reinforcement Schedules

212
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,...
212
Reinforcement01:23

Reinforcement

289
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:
289
Associative Learning01:27

Associative Learning

461
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
461
Instinctive Drift01:05

Instinctive Drift

254
Instinctive drift refers to the tendency of animals to revert to their innate behaviors despite repeated reinforcement. Breland and Breland demonstrated this concept in an experiment with a raccoon. The raccoon was trained to pick up two coins and place them in a container in exchange for food. Initially, the raccoon learned to associate the coins with food, making them a conditioned stimulus or a substitute for food. However, over time, the raccoon became less willing to put the coins into the...
254
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.8K
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...
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相关实验视频

Updated: Jul 25, 2025

An Open-Source Virtual Reality System for the Measurement of Spatial Learning in Head-Restrained Mice
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对于自动驾驶船只的时空空间反复增强学习.

Martin Waltz1, Ostap Okhrin2

  • 1Technische Universität Dresden, Chair of Econometrics and Statistics, esp. in the Transport Sector, Dresden, 01062, Germany.

Neural networks : the official journal of the International Neural Network Society
|June 26, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度Q网络,用于自主船只方向盘,增强避免碰撞和多艘船的导航. 该方法在复杂的海上场景中表现出强大的性能.

关键词:
自主地表车辆 自主地表车辆这就是 COLREG.深度强化学习的学习.现货货币是回应货币的

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科学领域:

  • 人工智能的人工智能
  • 机器人技术 机器人技术 机器人技术
  • 海事工程是海事工程.

背景情况:

  • 自主船舶导航需要先进的决策能力来处理复杂,动态的海上环境.
  • 现有的方法经常与多目标场景和部分可观测性扎,限制了它们在现实世界中的适用性.
  • 通过避免碰撞和遵守海事法规来确保安全至关重要.

研究的目的:

  • 为深度Q网络 (DQN) 提出一个时空循环神经网络架构,以增强自主船只转向.
  • 开发一个强大的系统,能够管理多个周围的船只和部分可观测性.
  • 将最新的碰撞风险指标和防止海上碰撞的国际法规 (COLREGs) 整合到代理人的决策过程中.

主要方法:

  • 开发一个与深度Q网络集成的时空循环神经网络.
  • 包含一个新的碰撞风险指标来评估情况.
  • 将 COLREG 规则纳入奖励函数设计中.
  • 使用"全天候"和Imazu (1987) 多艘船遭遇数据集进行验证.

主要成果:

  • 拟议的DQN架构有效地在复杂的场景中引导自动驾驶船舶.
  • 该系统在处理任意数量的目标船和部分可观测性方面表现出稳健性.
  • 性能比较显示在海上路径规划中优于人工潜力场和速度障碍方法.
  • 该架构与其他深度强化学习算法相兼容,包括演员关键框架.

结论:

  • 新的时空DQN架构在自主船舶路径规划和避免碰撞方面取得了重大进展.
  • 该方法为多代理海事场景提供了强大的和可适应的解决方案.
  • 这项工作为更安全,更有效的自主海事运营铺平了道路.