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
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.
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.
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