本书的目的是考虑大型且具有挑战性的多阶段决策问题，这些问题原则上可以通过动态规划和最优控制来解决，但它们的精确解决方案在计算上是难以处理的。本书讨论依赖于近似的解决方法，以产生具有足够性能的次优策略。这些方法统称为增强学习，也可以叫做近似动态规划和神经动态规划等。 本书的主题产生于最优控制和人工智能思想的相互作用。本书的目的之一是探索这两个领域之间的共同边界，并架设一座具有任一领域背景的专业人士都可以访问的桥梁。

Dimitri P. Bertseka,美国MIT终身教授，美国国家工程院院士，清华大学复杂与网络化系统研究中心客座教授。电气工程与计算机科学领域国际知名作者，著有《非线性规划》《网络优化》《凸优化》等十几本畅销教材和专著。

Preface Turning to the succor of modern computing machines, let us renounce all analytic tools. Richard Bellman [Bel57] From a teleological point of view the particular numerical solution of any particular set of equations is of far less importance than the understanding of the nature of the solution. Richard Bellman [Bel57] In this book we consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming (DP for short), but their exact solution is computationally intractable. We discuss solution methods that rely on approximations to produce suboptimal policies with adequate performance. These methods are collectively known by s...