「強化学習」のベストセラー入門書　20年ぶりの改訂！
人工知能における最も活発な研究分野の1つである強化学習の入門書、大幅改訂第2版。
初学者から研究者まで”強化学習のバイブル”として、世界中で広く使用されています。
初版同様この最新第2版でも、オンライン学習アルゴリズムに焦点を当てています。
新たに追加されたUCB、Expected Sarsa、Double Learningを含む、多くのアルゴリズムが紹介されています。
人工ニューラルネットワークやフーリエ基底、モンテカルロ木探索といった新しいトピックが追加され、さらに強化学習の心理学、神経科学との関係や、AlphaGo、AlphaGo Zero、Atariゲーム、IBM Watsonの賭け戦略など最新のケーススタディも取り上げられています。
強化学習のこの20年の進歩が解説されるとともに、将来の社会的インパクトについても議論されています。
人工知能、機械学習を学ぶ学生から強化学習に関心を持つ研究者、開発者にまで広くお薦めする1冊です。

"The second edition of Reinforcement Learning by Sutton and Barto comes at just the right time. The appetite for reinforcement learning among machine learning researchers has never been stronger, as the field has been moving tremendously in the last twenty years. If you want to fully understand the fundamentals of learning agents, this is the textbook to go to and get started with. It has been extended with modern developments in deep reinforcement learning while extending the scholarly history of the field to modern days. I will certainly recommend it to all my students and the many other graduate students and researchers who want to get the appropriate context behind the current excitement for RL."
-- Yoshua Bengio, Professor of Computer Science and Operations Research, University of Montreal (Co-Author of "Deep Learning" MIT Press c2016)

"I recommend Sutton and Barto's new edition of Reinforcement Learning to anybody who wants to learn about this increasingly important family of machine learning methods. This second edition expands on the popular first edition, covering today's key algorithms and theory, illustrating these concepts using real-world applications that range from learning to control robots, to learning to defeat the human world-champion Go player, and discussing fundamental connections between these computer algorithms and research on human learning from psychology and neuroscience."
-- Tom M. Mitchell, Professor of Computer Science, Carnegie-Mellon University (Author of "Machine Learning" McGraw-Hill c1997)