Approximate Dynamic Programming

出版者:Wiley-Blackwell

作者:Warren B. Powell

页数:656

出版时间:2011-11-18

价格:GBP 116.00

装帧:Hardcover

isbn号码:9780470604458

具体描述

Praise for the First Edition "Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners."

— Computing Reviews This new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problems Understanding approximate dynamic programming (ADP) is vital in order to develop practical and high-quality solutions to complex industrial problems, particularly when those problems involve making decisions in the presence of uncertainty. Approximate Dynamic Programming , Second Edition uniquely integrates four distinct disciplines—Markov decision processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully approach, model, and solve a wide range of real-life problems using ADP. The book continues to bridge the gap between computer science, simulation, and operations research and now adopts the notation and vocabulary of reinforcement learning as well as stochastic search and simulation optimization. The author outlines the essential algorithms that serve as a starting point in the design of practical solutions for real problems. The three curses of dimensionality that impact complex problems are introduced and detailed coverage of implementation challenges is provided. The Second Edition also features: A new chapter describing four fundamental classes of policies for working with diverse stochastic optimization problems: myopic policies, look-ahead policies, policy function approximations, and policies based on value function approximations A new chapter on policy search that brings together stochastic search and simulation optimization concepts and introduces a new class of optimal learning strategies Updated coverage of the exploration exploitation problem in ADP, now including a recently developed method for doing active learning in the presence of a physical state, using the concept of the knowledge gradient A new sequence of chapters describing statistical methods for approximating value functions, estimating the value of a fixed policy, and value function approximation while searching for optimal policies The presented coverage of ADP emphasizes models and algorithms, focusing on related applications and computation while also discussing the theoretical side of the topic that explores proofs of convergence and rate of convergence. A related website features an ongoing discussion of the evolving fields of approximation dynamic programming and reinforcement learning, along with additional readings, software, and datasets. Requiring only a basic understanding of statistics and probability, Approximate Dynamic Programming , Second Edition is an excellent book for industrial engineering and operations research courses at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who utilize dynamic programming, stochastic programming, and control theory to solve problems in their everyday work.

读后感

I forgot how I got to know this book, but I liked it a lot once I got a chance to read it. My favorite chapter is Chapter 5, which tells a general process of building a dynamic programming model. The most significant benefit of this books is that it bridges...

I forgot how I got to know this book, but I liked it a lot once I got a chance to read it. My favorite chapter is Chapter 5, which tells a general process of building a dynamic programming model. The most significant benefit of this books is that it bridges...

I forgot how I got to know this book, but I liked it a lot once I got a chance to read it. My favorite chapter is Chapter 5, which tells a general process of building a dynamic programming model. The most significant benefit of this books is that it bridges...

I forgot how I got to know this book, but I liked it a lot once I got a chance to read it. My favorite chapter is Chapter 5, which tells a general process of building a dynamic programming model. The most significant benefit of this books is that it bridges...

I forgot how I got to know this book, but I liked it a lot once I got a chance to read it. My favorite chapter is Chapter 5, which tells a general process of building a dynamic programming model. The most significant benefit of this books is that it bridges...

用户评价

这本书的结构安排非常精妙，它似乎是在引导读者逐步深入，而非直接扔给你一堆复杂的公式。开篇部分对随机过程和马尔可夫决策过程（MDP）的基础回顾扎实而全面，但绝不拖沓，很快就切入了主题——当我们面对高维度的状态和行动空间时，传统的价值迭代和策略迭代是如何迅速崩溃的。我个人尤其欣赏作者在处理“维度灾难”问题时的视角。他们没有满足于仅仅指出问题，而是系统性地展示了各种“聪明”的替代方案。比如，书中对函数逼近方法的引入和阐述，让我对如何用神经网络或其他基函数来表示价值函数有了新的认识。这种将现代机器学习技术与经典控制理论相结合的思路，是这本书的灵魂所在。我感觉作者的写作风格非常务实，每一部分内容的推进都是为了解决上一个章节遗留下的难题，形成了一个逻辑严密的探索链条。读完后，我感觉自己不只是掌握了几种算法，更是理解了一种解决复杂、不确定性决策问题的思维框架。

这本书的叙事节奏感非常强，读起来不像在啃一本学术专著，更像是在跟随一位经验丰富的导师进行一次系统的项目指导。它最让人眼前一亮的地方，是对“探索与利用”（Exploration vs. Exploitation）这个经典难题的系统性梳理。很多资料只是泛泛而谈 UCB（上置信界）或者 $epsilon$-贪婪策略，但这本书深入剖析了这些策略背后的概率论基础，并展示了如何将这些思想应用于更复杂的策略梯度方法中。我特别喜欢它在引入策略迭代算法时所使用的类比，那种将策略看作一个可以被不断打磨和优化的“工具集”的观念，极大地激发了我对改进现有控制系统的热情。此外，书中对无模型学习（Model-Free Learning）的详尽讨论，完美地契合了当下许多实际应用中，我们无法获得精确环境模型的现实困境。这种贴近现实挑战的写作态度，让每一个在实际工程中挣扎的读者都能从中找到共鸣和指引。

这本书的价值远超出了对特定算法的介绍，它真正构建的是一套应对不确定性、追求次优解的哲学体系。我花了不少时间消化其中关于大规模系统和多智能体环境的部分，那里的挑战性是指数级增长的。作者在处理这些前沿课题时，展示了极大的勇气和清晰的逻辑。他们没有回避这些问题在理论上的棘手性，而是坦诚地列出了当前学界正在探索的几条主要路径，并对每条路径的未来潜力给出了审慎的评估。这种开放和批判性的态度，比提供一个“万能药”式的答案要宝贵得多。对我而言，这本书更像是一份路线图，它清晰地勾勒出了该领域的核心挑战、已经取得的成就，以及未来可能的研究方向。它不仅仅是一本“怎么做”的书，更是一本“为什么我们要做这些尝试”的思想基石。对于任何希望在这个领域深耕下去的研究者来说，它都是不可或缺的引路石。

这本书的封面设计简洁却富有深意，那种淡淡的灰蓝色调，配上现代感的字体，立刻让人感觉这不是一本普通的教科书，更像是一扇通往复杂世界的大门。我最初被这本书吸引，是因为它在算法领域那种近乎“魔法”般的处理能力。我一直以来都在研究决策优化问题，尤其是在状态空间巨大、计算资源有限的情况下，如何找到一个“足够好”的解，而不是追求那个理论上最优却遥不可及的答案。这本书显然不是空泛地讨论理论，而是深入到实际操作的层面，它不像其他一些经典著作那样把重点完全放在证明的严谨性上，而是更侧重于“如何做”以及“为什么这样做有效”。书中对各种启发式方法的介绍非常到位，尤其是对迭代过程的细致拆解，让我对传统动态规划的局限性有了更深刻的理解。我记得有几个章节，作者用非常生动的例子来解释 Bellman 方程在复杂环境下的近似应用，那种将抽象数学概念具象化的能力，是这本书最吸引我的地方之一。它成功地架起了一座桥梁，连接了纯粹的数学理论和工程实践的需求。

坦率地说，我对这类偏向计算和优化的书籍通常抱有一定程度的敬畏，因为它们往往晦涩难懂，需要读者具备深厚的数学背景。然而，这本书在保证理论深度的同时，却展现出令人惊讶的“可读性”。作者在解释核心算法时，非常注重直觉的培养。比如，在描述 Monte Carlo 方法和 TD（时序差分）学习的对比时，他们并没有仅仅停留在公式的差异上，而是通过模拟实际环境中的信息获取过程，让读者真切地体会到“在线学习”和“样本估计”各自的优势与劣势。书中穿插的那些小小的“洞察”和“权衡分析”，是教科书中不常有的宝贵财富。它们帮助读者理解，在真实世界的应用中，选择哪种近似方法往往涉及到对计算成本、收敛速度和解质量的复杂权衡。这种兼顾理论严谨性和工程实用性的平衡感，让这本书在我的书架上脱颖而出，成为了我时常翻阅的参考书。