强化学习同时体现了一种思想。这在多本强化学习的著作中都有类似的说明。
这本 深度强化学习 书的前言如下，特别交其中英文摘出来
- 出版社： [清华大学出版社
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ISBN：9787302659792
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近期，深度强化学习引起了广泛关注。人们在各个领域中取得了惊人成果，如自动驾驶、电子竞技、分子重组和机器人技术。在所有这些领域，电脑程序已经学会了解决困难的问题。它们学会了驾驶模型直升机，还可以完成像循环和翻滚这样的特技动作。在某些应用中，它们甚至比人类最优秀的操作者表现得更好，例如，在Atari游戏、围棋、扑克和星际争霸中。
Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have learned to solve difficult problems. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls. In some applications they have even become better than the best humans, such as in Atari, Go, poker, and StarCraft.
- 深度强化学习探索复杂环境的方式，有点像小孩子玩耍时尝试不同的事情，得到反馈后再试一次。计算机好像真的具有一些人类学习的能力；深度强化学习触及人类的梦想。
- The way in which deep reinforcement learning explores complex environments reminds us how children learn, by playfully trying out things, getting feedback, and trying again. The computer seems to truly possess aspects of human learning; deep reinforcement learning touches the dream of artificial intelligence.
研究领域的成功引起了教育者的关注，各个大学相继开始推出相关课程。本书的目标是全面介绍深度强化学习这个领域。它是为人工智能专业的研究生，以及想要更好地了解深度强化学习方法和挑战的研究人员和从业者编写的。我们假设读者具备计算机科学和人工智能方面的本科水平，并对这些内容有基本的了解；本书使用的编程语言是Python。
The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The book is written for graduate students of artificial intelligence, and for researchers and practitioners who wish to better understand deep reinforcement learning methods and their challenges. We assume an undergraduate-level of understanding of computer science and artificial intelligence; the programming language of this book is Python.
我们将描述深度强化学习的基础、算法和应用。本书将涵盖构成该领域基础的已建立的无模型和有模型方法。由于该技术发展迅速，本书还将涵盖更高级的主题：深度多智能体强化学习、深度分层强化学习和深度元学习。
希望本书会给你带来与许多研究人员一样的喜悦，他们在开发算法、最终让它们运行起来的过程中感受到了无比的快乐！
We describe the foundations, the algorithms, and the applications of deep reinforcement learning. We cover the established model-free and model-based methods that form the basis of the field. Developments go quickly, and we also cover more advanced topics: deep multi-agent reinforcement learning, deep hierarchical reinforcement learning, and deep meta learning.
We hope that learning about deep reinforcement learning will give you as much joy as the many researchers experienced when they developed their algorithms, finally got them to work, and saw them learn!
Leiden, The Netherlands Aske Plaat January 2022
