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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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利用与任务相关的潜伏的预测来实现交互式视觉导航.

Jiwei Shen, Liang Yuan, Yue Lu

    IEEE transactions on neural networks and learning systems
    |December 1, 2023
    PubMed
    概括

    本研究介绍了与任务相关潜伏的预测 (PTRLs),这是一个自我监督的强化学习 (RL) 框架. 通过提高环境表示和样本效率,PTRLs可以增强嵌入式代理导航,以获得更好的准确性和概括性.

    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
    • 计算机视觉 计算机视觉

    背景情况:

    • 交互式视觉导航 (IVN) 任务需要有形代理来学习环境相互作用以实现目标.
    • 当前的强化学习 (RL) 方法由于在部分可观测的环境中对环境的不高效表示而与IVN扎.

    研究的目的:

    • 引入一个灵活的自我监督的RL框架,任务相关潜伏的预测 (PTRLs),以提高IVN任务性能.
    • 增强结构化环境动态和顺序观测表示的学习.

    主要方法:

    • PTRL框架使用自主监督学习来根据行动预测代理人的下一个姿势.
    • 包含注意力和记忆模块,用于空间时间依赖性利用.
    • 采用状态价值提升模块,通过输入扰动和价值函数规范化适应新环境.

    主要成果:

    • PTRL在提高IVN任务的准确性和概括能力方面表现出卓越的表现.
    • 在RL网络培训中,样本效率通过模块化培训和层次分解显著提高.

    结论:

    • 拟议的PTRLs框架提供了一个强大的解决方案,用于改善复杂环境中的体内代理导航.

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  • PTRLs有效地解决了传统RL方法在IVN学习环境表示中的局限性.