一种用于多阶段决策的鲁棒二元非线性求解器

中文摘要 AI 辅助

本文描述了两个方面的发展。第一是一个多阶段决策框架，它允许以通用方式表述各种序贯决策问题。第二涉及一种鲁棒的随机求解过程，旨在处理由所提出的决策框架产生的二元非线性优化问题。该方法采用带有增量概率学习和强化的蒙特卡洛搜索。本文描述了这两个发展以及一个示范性应用实例。关键词：多阶段、决策、二元、非线性系统、概率与强化学习。

英文摘要

This document describes two developments. The first is a Multi-stage Decision Framework that permits the formulation of various sequential decision-making problems in a generic manner. The second concerns a robust stochastic solution procedure designed to handle the binary nonlinear optimization problem stemming from the proposed decision framework. The method adopts Monte-Carlo search with incremental probabilistic learning with reinforcement. The two developments are described herein along with a demonstrative application example. Key words: Multi-stage, decisions, binary, nonlinear systems, probabilistic and reinforcement learning

发表机构

• Schlumberger-Doll Research（施伦伯格-多尔研究所）

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