Worth: $143.00 - $128.88
(as of Aug 28, 2025 17:06:59 UTC – Particulars)
The considerably expanded and up to date new version of a broadly used textual content on reinforcement studying, one of the crucial lively analysis areas in synthetic intelligence.
Reinforcement studying, one of the crucial lively analysis areas in synthetic intelligence, is a computational strategy to studying whereby an agent tries to maximise the entire quantity of reward it receives whereas interacting with a posh, unsure atmosphere. In Reinforcement Studying, Richard Sutton and Andrew Barto present a transparent and easy account of the sphere’s key concepts and algorithms. This second version has been considerably expanded and up to date, presenting new subjects and updating protection of different subjects.
Like the primary version, this second version focuses on core on-line studying algorithms, with the extra mathematical materials set off in shaded containers. Half I covers as a lot of reinforcement studying as attainable with out going past the tabular case for which actual options might be discovered. Many algorithms introduced on this half are new to the second version, together with UCB, Anticipated Sarsa, and Double Studying. Half II extends these concepts to operate approximation, with new sections on such subjects as synthetic neural networks and the Fourier foundation, and gives expanded remedy of off-policy studying and policy-gradient strategies. Half III has new chapters on reinforcement studying’s relationships to psychology and neuroscience, in addition to an up to date case-studies chapter together with AlphaGo and AlphaGo Zero, Atari recreation taking part in, and IBM Watson’s wagering technique. The ultimate chapter discusses the long run societal impacts of reinforcement studying.
Writer : Bradford Books
Publication date : Nov. 13 2018
Version : 2nd ed.
Language : English
Print size : 552 pages
ISBN-10 : 0262039249
ISBN-13 : 978-0262039246
Merchandise weight : 1.2 kg
Dimensions : 18.42 x 3.96 x 23.5 cm
Greatest Sellers Rank: #105,923 in Books (See Prime 100 in Books) #4 in Fuzzy Logic Algorithms #22 in Algorithms Textbooks #31 in A.I. Neural Networks
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