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深信马尔科夫模型用于POMDP推理推理.

Giacomo Arcieri1, Konstantinos G Papakonstantinou2, Daniel Straub3

  • 1Institute of Structural Engineering, ETH Zürich, Zürich, 8093, Switzerland.

Neural networks : the official journal of the International Neural Network Society
|December 12, 2025
PubMed
概括

本研究介绍了深度信念马尔科夫模型 (DBMM),这是一个新的深度学习架构,用于在部分可观察的马尔科夫决策过程 (POMDP) 问题中高效推断. 在不确定性下,DBMM能够有效地做出决策,在复杂的环境中性能优于现有的方法.

关键词:
他们的信仰.深度马尔科夫模型的模型.深度学习是一种深度学习.基础设施管理的基础设施管理.部分可观测的马尔科夫决策过程.变化推理的推理是变化的.

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 强化学习是一种强化学习.

背景情况:

  • 部分可观察的马尔科夫决策过程 (POMDPs) 对于在不确定性下进行顺序决策至关重要.
  • 对于高维的POMDPs,现有的推理方法经常因可扩展性和缺乏基本真相状态数据而扎.
  • 深度学习为模拟这些问题固有的复杂,非线性动态提供了潜力.

研究的目的:

  • 引入一种新的深度学习架构,即深度信念马尔科夫模型 (DBMM),用于高效的POMDP推理.
  • 开发一种不依赖于模型的方法来处理复杂,高维和部分可观测的环境.
  • 仅使用观测数据实现强有力的信念推断,克服精确计算和采样方法的局限性.

主要方法:

  • 开发了深度信念马尔科夫模型 (DBMM),将深度马尔科夫模型扩展到POMDP框架.
  • 利用变量推理方法,直接从观察数据中有效地推断信念.
  • 利用神经网络推断和模拟非线性系统动态,容纳高维度和混合变量类型.

主要成果:

  • 在基准POMDP问题中,DBMM表现出高效的,与模型配方无关的推理能力,以离散和连续变量为基准.
  • 神经网络参数根据数据可用性有效地更新,允许动态适应.
  • 一个由DBMM信念指导的RL代理显著超过了无模型基线,在下游任务中实现了近乎最佳的性能.

结论:

  • 在复杂的POMDP中,DBMM为信念推断提供了有效的解决方案,克服了传统方法的可扩展性和数据限制.
  • 架构的推断信念的能力使得有效的POMDP解决方案的推导成为可能.
  • 在具有挑战性的决策场景中,DBMM显示出显著的实际实用性,提高了强化学习代理的性能.