混合状态和动作变量的因子MDPs求解

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摘要翻译：
具有连续变量和离散变量的大型决策问题的有效表示和求解是自动化决策支持系统设计者面临的最重要的挑战之一。本文给出了一个新的混合因子马尔可夫决策过程(MDP)模型，该模型允许这些问题的紧致表示，并给出了一个新的混合近似线性规划(HALP)框架，该框架允许它们的有效解。HALP的核心思想是通过基函数的线性组合逼近最优值函数，并通过线性规划优化其权值。我们分析了该方法的理论和计算方面，并证明了它在几个混合优化问题上的推广潜力。
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英文标题：
《Solving Factored MDPs with Hybrid State and Action Variables》
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作者：
C. Guestrin, M. Hauskrecht, B. Kveton
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Efficient representations and solutions for large decision problems with continuous and discrete variables are among the most important challenges faced by the designers of automated decision support systems. In this paper, we describe a novel hybrid factored Markov decision process (MDP) model that allows for a compact representation of these problems, and a new hybrid approximate linear programming (HALP) framework that permits their efficient solutions. The central idea of HALP is to approximate the optimal value function by a linear combination of basis functions and optimize its weights by linear programming. We analyze both theoretical and computational aspects of this approach, and demonstrate its scale-up potential on several hybrid optimization problems.
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PDF链接：
https://arxiv.org/pdf/1110.0028

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关键词：DPS MDP 模型 HALP 计算 problems 动作