The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. InReinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

强化学习是人工智能中最活跃的研究领域之一，这是一本被广泛使用的关于强化学习的文本的大幅扩展和更新的新版本。

强化学习是人工智能中最活跃的研究领域之一，是一种计算性的学习方法，代理人在与复杂的、不确定的环境进行互动时，试图使其获得的奖励总量最大化。在《强化学习》中，理查德-萨顿和安德鲁-巴托对该领域的关键思想和算法作了清晰而简单的说明。本书的第二版进行了大幅扩充和更新，提出了新的主题，并更新了对其他主题的覆盖。

与第一版一样，第二版的重点是核心在线学习算法，更多的数学材料被放在阴影框中。第一部分涵盖了尽可能多的强化学习的内容，而没有超出可以找到精确解的表格情况。这一部分介绍的许多算法是第二版的新算法，包括UCB、预期萨萨和双重学习。第二部分将这些思想扩展到函数近似，有关于人工神经网络和傅里叶基础等主题的新章节，并对非政策学习和政策梯度方法进行了扩展处理。第三部分有关于强化学习与心理学和神经科学关系的新章节，以及更新的案例研究章节，包括AlphaGo和AlphaGo Zero、Atari游戏和IBM Watson的赌注策略。最后一章讨论了强化学习的未来社会影响。