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相关概念视频

Reinforcement01:23

Reinforcement

199
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:
199
Purposive Learning01:22

Purposive Learning

107
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
107
Observational Learning01:12

Observational Learning

158
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...
158
Propagation of Action Potentials01:23

Propagation of Action Potentials

5.5K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
5.5K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

105
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...
105
Reinforcement Schedules01:24

Reinforcement Schedules

139
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,...
139

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相关实验视频

Updated: Jun 18, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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通过潜在的现场,以生成性次目标为导向的多代理强化学习.

Shengze Li1, Hao Jiang1, Yuntao Liu1

  • 1Academy of Military Science, Beijing, 100000, China.

Neural networks : the official journal of the International Neural Network Society
|August 1, 2024
PubMed
概括

本研究引入了一种新的潜在场子基于子目标的多代理强化学习 (PSMA) 方法,以统一多代理强化学习 (MARL) 中的学习目标. 通过使用潜在的子目标生成和实现领域,PSMA提高了在稀疏奖励任务中的代理学习速度和有效性.

关键词:
多个代理强化学习学习的多个代理强化学习.潜在场是一个潜在场.下一个目标是生成子目标.

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相关实验视频

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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:

  • 多代理强化学习 (MARL) 使用子目标在稀疏奖励环境中加速代理学习.
  • 现有的MARL方法往往在子目标生成和实现之间缺乏一致性,这阻碍了学习的有效性.

研究的目的:

  • 提出一种新的潜力场子基于目标的多代理强化学习 (PSMA) 方法.
  • 在MARL中统一子目标生成和子目标达成阶段的学习目标.

主要方法:

  • 引入了潜在场 (PF) 来表示代理状态并测量代理之间的相互作用.
  • 开发了一个状态到PF的表示模型和一个使用经验重复缓冲区的子目标选择器.
  • 定义了一个内在的奖励函数来引导代理人实现子目标,同时最大限度地提高联合行动价值.

主要成果:

  • 拟议的PSMA方法有效地统一了两阶段的学习目标.
  • 与最先进的MARL方法相比,PSMA表现优越.
  • 在稀疏的奖励设置中,在StarCraft II微管理 (SMAC) 和谷歌研究足球 (GRF) 任务中取得了显著的改进.

结论:

  • 该PSMA方法提供了一个统一的方法,在马尔的子目标学习.
  • 潜在场提供了一个有效的机制来表示代理状态和相互作用.
  • 在复杂的,稀疏的奖励环境中,PSMA显著提高了MARL的性能.