Reinforcement Learning: An Introduction, 2/e (Hardcover)

Richard S. Sutton, Andrew G. Barto



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. In Reinforcement 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.



強化學習是一種計算方法,代理人在與複雜且不確定的環境互動時,試圖最大化獲得的總獎勵量。在《強化學習》一書中,Richard Sutton和Andrew Barto清晰而簡單地介紹了該領域的關鍵思想和算法。這本第二版經過了大幅擴充和更新,介紹了新的主題並更新了其他主題的內容。

與第一版一樣,這本第二版專注於核心的在線學習算法,數學材料以陰影框的形式呈現。第一部分盡可能涵蓋了強化學習的內容,但沒有超出可以找到精確解的表格案例。本部分介紹的許多算法都是第二版的新內容,包括UCB、Expected Sarsa和Double Learning。第二部分將這些思想擴展到函數逼近,新增了關於人工神經網絡和傅立葉基底等主題的章節,並對離策略學習和策略梯度方法進行了擴展處理。第三部分新增了關於強化學習與心理學和神經科學的關係的章節,以及一個更新的案例研究章節,包括AlphaGo和AlphaGo Zero、Atari遊戲和IBM Watson的下注策略。最後一章討論了強化學習對未來社會的影響。