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相关概念视频

Reinforcement01:23

Reinforcement

186
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:
186
Reinforcement Schedules01:24

Reinforcement Schedules

135
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,...
135

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通过在样本中的优势规范化来改善机器人操纵的离线增强学习.

Chengzhong Ma, Deyu Yang, Tianyu Wu

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    概括
    此摘要是机器生成的。

    离线增强学习 (RL) 通过从固定的数据集学习来提高机器人的安全性和效率. 一种新的方法,即样本优势规范化 (ISAR),可以在不需要复杂的调整的情况下提高性能.

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    科学领域:

    • 机器人技术 机器人技术 机器人技术
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 离线增强学习 (RL) 能够从固定的数据集中学习政策,避免风险的实时探索,以提高机器人的效率和安全性.
    • 现有的线下RL方法往往与分发外的行动扎,导致受约束的政策和增加的复杂性.
    • 将线下RL适应机器人操纵需要尽量减少变化,避免分布之外的行动评估.

    研究的目的:

    • 以最少的修改来适应线下强化学习用于机器人操纵.
    • 通过避免分发外的行动评估,减轻线下RL中看不见的行动的影响.
    • 引入一个简单,高效,易于实施的方法,用于机器人线下RL在机器人.

    主要方法:

    • 使用在样本优势规范化 (ISAR) 改进离线RL.
    • ISAR只使用数据集样本来学习状态-值函数,以回归最佳的动作-值函数,减轻未见的动作影响.
    • 根据样本内价值估计计算优势函数,并在政策更新期间结合行为克隆 (BC) 正规化.

    主要成果:

    • 在D4RL机器人基准和稀疏奖励机器人任务上,ISAR的性能与最先进的算法相美.
    • 该方法表现出卓越的性能,不需要复杂的超参数调整或过度的训练时间.
    • ISAR的有效性在现实世界的机器人平台上得到验证.

    结论:

    • ISAR提供了一种简单而有效的方法,以提高机器人操纵离线RL中的样本效率.
    • 该方法成功地解决了未见的行动所带来的挑战,而没有引入显著的复杂性.
    • ISAR显示出在现实世界机器人系统中实际应用的巨大潜力.