Scholarlink

<p>The Bellman equation, with a resource-consuming solving process, plays a fundamental role in formulating and solving dynamic optimization problems. The realization of the Bellman solver with memristive computing-in-memory (MCIM) technology, is significant for implementing efficient dynamic decision-making. However, the iterative nature of the Bellman equation solving process poses a challenge for efficient implementation on MCIM systems, which excel at vector-matrix multiplication (VMM) operations but are less suited for iterative algorithms. In this work, by incorporating the temporal dimension and transforming the solution into recurrent dot product operations, a memristive Bellman solver (MBS) is proposed, facilitating the implementation of the Bellman equation solving process with efficient MCIM technology. The MBS effectively reduces the iteration numbers and which further enhanced by approximated solutions leveraging memristor noise. Finally, the path planning tasks are used to verify the feasibility of the proposed MBS. The theoretical derivation and experimental results demonstrate that the MBS effectively reduces the iteration cycles, facilitating the solving efficiency. This work could be a sound of choice for developing high-efficiency decision-making systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Memristive Bellman solver for decision-making

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月27日

查看全文
  • Zhe Feng,
  • Zuheng Wu,
  • Jianxun Zou,
  • Lingli Cheng,
  • Xiaolong Zhao,
  • Xumeng Zhang,
  • Jian Lu,
  • Cong Wang,
  • Yilin Wang,
  • Haochen Wang,
  • Wenbin Guo,
  • Zhibin Qian,
  • Yunlai Zhu,
  • Zuyu Xu,
  • Yuehua Dai,
  • Qi Liu

摘要

The Bellman equation, with a resource-consuming solving process, plays a fundamental role in formulating and solving dynamic optimization problems. The realization of the Bellman solver with memristive computing-in-memory (MCIM) technology, is significant for implementing efficient dynamic decision-making. However, the iterative nature of the Bellman equation solving process poses a challenge for efficient implementation on MCIM systems, which excel at vector-matrix multiplication (VMM) operations but are less suited for iterative algorithms. In this work, by incorporating the temporal dimension and transforming the solution into recurrent dot product operations, a memristive Bellman solver (MBS) is proposed, facilitating the implementation of the Bellman equation solving process with efficient MCIM technology. The MBS effectively reduces the iteration numbers and which further enhanced by approximated solutions leveraging memristor noise. Finally, the path planning tasks are used to verify the feasibility of the proposed MBS. The theoretical derivation and experimental results demonstrate that the MBS effectively reduces the iteration cycles, facilitating the solving efficiency. This work could be a sound of choice for developing high-efficiency decision-making systems.