Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series)
暫譯: 基於模擬的優化：參數優化技術與強化學習（運籌學/計算機科學介面系列）

Abhijit Gosavi

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series)
  • 出版商: Springer
  • 出版日期: 2014-10-30
  • 售價: $7,310
  • 貴賓價: 9.5 折 $6,945
  • 語言: 英文
  • 頁數: 508
  • 裝訂: Hardcover
  • ISBN: 1489974903
  • ISBN-13: 9781489974907
  • 相關分類: Reinforcement、DeepLearning、Computer-Science

    • 海外代購書籍(需單獨結帳)

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商品描述

This book introduces to the reader the evolving area of simulation-based optimization, also known as simulation optimization. The book should serve as an accessible introduction to this topic and requires a background only in elementary mathematics. It brings the reader up to date on cutting-edge advances in simulation-optimization methodologies, including dynamic controls, also called Reinforcement Learning (RL) or Approximate Dynamic Programming (ADP), and static optimization techniques, e.g., Simultaneous Perturbation, Nested Partitions, Backtracking Adaptive Search, Response Surfaces, and Meta-Heuristics. Special features of this book include:

Stochastic Control Optimization:
  • An Accessible Introduction to Reinforcement Learning Techniques for Solving Markov Decision Processes (MDPs), with Step-by-Step Descriptions of Numerous Algorithms, e.g., Q-Learning, SARSA, R-SMART, Actor-Critics, Q-P-Learning, and Classical Approximate Policy Iteration
  • A Detailed Discussion on Dynamic Programing for Solving MDPs and Semi-MDPs (SMDPs), Including Steps for Value Iteration and Policy Iteration
  • An Introduction to Function Approximation with Reinforcement Learning
  • An In-Depth Treatment of Reinforcement Learning Methods for SMDPs, Average Reward Problems, Finite Horizon Problems, and Two Time Scales
  • Computer Programs (available online)
  • A Gentle Introduction to Convergence Analysis of Simulation Optimization Methods via Banach Fixed Point Theory and Ordinary Differential Equations (ODEs)
Stochastic Static Optimization:
  • A Step-by-Step Description of Stochastic Adaptive Search Algorithms, e.g., Simultaneous Perturbation, Nested Partitions, Backtracking Adaptive Search, Stochastic Ruler, and Meta-Heuristics, e.g., Simulated Annealing, Tabu Search, and Genetic Algorithms 
  • A Clear and Simple Introduction to the Methodology of Neural Networks 
The book ends with a chapter on case studies that explain how these methods can be applied in real-world settings; an online repository of computer programs that can be downloaded from a website is also available.

The book was written for students and researchers in the fields of engineering (industrial, electrical, and computer), computer science, operations research, management science, and applied mathematics.  An attractive feature of this book is its accessibility to readers new to this topic.

商品描述(中文翻譯)

本書向讀者介紹了不斷發展的基於模擬的優化領域，也稱為模擬優化。本書應該作為這一主題的易於理解的入門書籍，僅需具備基礎數學的背景。它使讀者了解模擬優化方法的前沿進展，包括動態控制，也稱為強化學習（Reinforcement Learning，RL）或近似動態規劃（Approximate Dynamic Programming，ADP），以及靜態優化技術，例如同時擾動、嵌套劃分、回溯自適應搜索、響應曲面和元啟發式。本書的特點包括：

隨機控制優化：


  • 對於解決馬可夫決策過程（MDPs）的強化學習技術的易於理解的介紹，並逐步描述多種算法，例如Q-Learning、SARSA、R-SMART、Actor-Critics、Q-P-Learning和經典的近似策略迭代

  • 對於解決MDPs和半馬可夫決策過程（SMDPs）的動態規劃的詳細討論，包括價值迭代和策略迭代的步驟

  • 強化學習中的函數近似介紹

  • 對於SMDPs、平均獎勵問題、有限時間範圍問題和雙時間尺度的強化學習方法的深入探討


  • 計算機程序（可在線獲取）

  • 通過巴拿赫不動點理論和常微分方程（ODEs）對模擬優化方法的收斂分析的簡單介紹

隨機靜態優化：

  • 隨機自適應搜索算法的逐步描述，例如同時擾動、嵌套劃分、回溯自適應搜索、隨機尺規和元啟發式，例如模擬退火、禁忌搜索和遺傳算法

  • 對於神經網絡方法的清晰簡單介紹

本書以一章案例研究結尾，解釋這些方法如何應用於現實世界的情境；還提供了一個可從網站下載的計算機程序的在線庫。

本書是為工程（工業、電氣和計算機）、計算機科學、運籌學、管理科學和應用數學領域的學生和研究人員撰寫的。本書的一個吸引人的特點是其對於新手讀者的易於理解性。

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