用非马科夫分散多样化政策来扩大解决方案空间
IEEE transactions on pattern analysis and machine intelligence
|September 6, 2024
概括
本研究引入了一种新的分散方法,以扩大非马尔科夫决策过程 (非MDP) 的强化学习 (RL) 中的解决方案空间. 这种方法产生了更多的多样化和强大的政策,在非MDP和MDP环境中表现优于现有的方法.
科学领域:
- 强化学习是一种强化学习.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 标准的强化学习 (RL) 依赖于马尔科夫决策过程 (MDP),但现实世界的场景往往涉及非MDP,奖励取决于国家行动历史.
- 非MDPs由于非结构化的解决方案空间,不同的历史信息和时间依赖性,导致非等效解决方案的挑战.
- 政策多样性对于在RL环境中强大,适应性强,创新的解决问题至关重要.
研究的目的:
- 通过政策分散扩大解决方案空间,开发一种方法来推导非MDP的多样化解决方案.
- 为了获得更多样化的政策,表现出强的绩效.
- 通过加强政策探索和适应能力来解决RL非MDPs的局限性.
主要方法:
- 利用基于变压器的模型从状态动作序列中学习政策嵌入,利用变压器处理顺序数据和远程依赖的能力.
- 从堆叠的政策嵌入中构建了一个分散矩阵,以测量政策多样性,并在解决方案空间内诱导分散.
- 证明正确的确定的分散矩阵有效地扩大了政策分歧,导致嵌入的多样化分布.
主要成果:
- 拟议的分散方案成功地扩大了解决方案空间,产生了更具表达性和多样化的政策.
- 实验结果表明,与最近的学习基线相比,该方法在非MDP和MDP环境中实现了更强大的性能.
- 这种方法有效地增强了政策多样性,因为它增加了跨政策的分歧.
结论:
- 基于变压器的政策分散方法在为非MDPs产生多样化的政策方面是有效的.
- 通过分散扩大溶液空间,从而提高RL剂的强度和适应性.
- 这项工作为在强化学习中解决复杂的非MDP环境提供了有希望的方向.
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