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一种基于分解优化的多目标增强学习算法,用于获得非凸的帕雷托前线

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    此摘要是机器生成的。

    本研究介绍了多目标强化学习 (MORL) 的新型非线性算法. 它有效地解决了复杂的决策问题中的非凸起的帕雷托阵线.

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    科学领域:

    • 人工智能
    • 机器学习
    • 优化情况

    背景情况:

    • 多目标强化学习 (MORL) 在多目标马尔科夫决策过程中寻求帕雷托前线 (PF).
    • 现有的MORL算法与非凸 PFs相斗争,限制了它们的适用性.
    • 这种局限性阻碍了在复杂场景中发现多样化,最佳的政策.

    研究的目的:

    • 提出一种新的非线性MORL算法,MORL/D-VR,能够处理非凸 PF.
    • 提供一个理论上的保证,无论PF的形状如何,都能找到最佳的帕雷托政策.
    • 加强政策梯度方法以提高绩效和多样性.

    主要方法:

    • 通过切比切夫的方法将MOMDP分解为单一目标的MDP.
    • 应用改进的政策梯度算法,预期的公用事业政策梯度 (EUPG).
    • 实施减差技术和重量向量调整以提高性能.

    主要成果:

    • 对于非凸 PF,MORL/D-VR 证明了理论上的帕雷托最佳性.
    • 该算法在凸和非凸的PF问题上实现了理想的性能.
    • 实验结果显示MORL/D-VR的性能优于当前最先进的MORL算法.

    结论:

    • 在处理非凸 PF 时,MORL/D-VR 有效地克服了现有的 MORL 算法的局限性.
    • 提出的方法为在复杂的MOMDP中实现帕雷托最佳性提供了理论基础.
    • MORL/D-VR代表了MORL的重大进步,改善了政策发现和绩效.